Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and (...) ethicality on data scientists, or data engineer during data mining process. This paper review issues of legality, privacy, and ethicality in data mining, review processes of Data mining, and also proposes solution to current ethical and privacy issue in data mining. It introduces a new method which enforces data mining without infringing on the privacy of individual or consumer whose data are being used. The sole aim of this paper is to propose a new method of mining data which restricts scientists within the constraints of legality, privacy, and ethicality. (shrink)

In the privacy preserving data mining, the utility mining casts a very vital part. The objective of the suggested technique is performed by concealing the high sensitive item sets with the help of the hiding maximum utility item first (HMUIF) algorithm, which effectively evaluates the sensitive item sets by effectively exploiting the user defined utility threshold value. It successfully attempts to estimate the sensitive item sets by utilising optimal threshold value, by means of the grey wolf optimisation (...) (GWO) algorithm. The optimised threshold value is then checked for its performance analysis by employing several constraints like the HF, MC and DIS. The novel technique is performed and the optimal threshold resultant item sets are assessed and contrasted with those of diverse optimisation approaches. The novel HMUIF considerably cuts down the calculation complication, thereby paving the way for the enhancement in hiding performance of the item sets. (shrink)

Motivation: • Breaking barriers in publishing demands a proactive attitude • Open data, open review and open dialogue in making social sciences plausible .

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine (...) learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine (...) learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue (...) that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

Fixing the problem won’t be easy, but humans’ sharpened focus on an emerging philosophy of data might give us some clue about where we will be heading for.

In the end of the course the student will be able to: • Understand the functioning of data mining tools and their contributions to managerial professions • Master the use of dynamic search tools on the open web and on the dark web. • Use the proper tools according to the objectives sought • Master the latest trends and innovations in Business Analytics • Analyze the opportunities offered in terms of data mining by artificial intelligence and (...) IoT. (shrink)

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to (...) perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

"Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the (...) analysis will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest (...) over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

Historic preservation, which is a vital act for conveying people’s understanding of the past, such as events, ideas, and places to the future, allows people to preserve history for future generations. Additionally, since the historic properties are currently concentrated in urban areas, an urban-oriented approach will contribute to the issue. Hence, public awareness is a key factor that paves the way for this conservation. Public history, a history with a public audience and special methods of representation, can serve society in (...) this regard. Historical video games, which get their game objectives from the past, can be considered a form of public history, raising people’s awareness and letting them gain historical experience. While plenty of studies focused on this content with various approaches, this study takes the topic under consideration with a player-oriented focus. The research uses the Steam video game digital distribution service database to explore the existing historical games based on their genre, player scale, dimension, VR compatibility attributes, and player rating. The database includes 1,148 games published between 2002 and 2022, filtered as historical by Steam tags and sorted by player review. For the data collection, the study uses the Octoparse web scraping tool for data extraction, and for the data analysis, it uses RapidMiner Studio data mining software. The study aims to provide a framework applicable to the development of commercially playable historical video games contributing to heritage preservation by raising public awareness in the context of public history. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for (...) the purposes of making computers understand medical natural language. (shrink)

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student (...) success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students. (shrink)

Customer feedback and reviews are rich sources of information that reflect the sentiments and experiences of consumers, especially in the financial sector. Mining customer sentiments from these textual data sources provides valuable insights for improving services, identifying emerging issues, and predicting customer satisfaction. This paper proposes a novel approach to mining customer sentiments from financial feedback and reviews, leveraging advanced natural language processing (NLP) techniques, sentiment analysis algorithms, and machine learning models. We discuss methods for preprocessing financial (...) text data, feature extraction, sentiment classification, and visualization of sentiment trends. Additionally, we explore the application of sentiment analysis in understanding customer behaviour, improving financial product offerings, and enhancing customer support strategies. Experimental results demonstrate the effectiveness of the proposed methods in accurately predicting customer sentiments and generating actionable insights. This paper also suggests directions for future research, focusing on real-time sentiment monitoring and integration with personalized financial services. (shrink)

This chapter addresses the morality of two types of national security electronic surveillance (SIGINT) programs: the analysis of communication “metadata” and dragnet searches for keywords in electronic communication. The chapter develops a standard for assessing coercive government action based on respect for the autonomy of inhabitants of liberal states and argues that both types of SIGINT can potentially meet this standard. That said, the collection of metadata creates opportunities for abuse of power, and so judgments about the trustworthiness and competence (...) of the government engaging in the collection must be made in order to decide whether metadata collection is justified in a particular state. Further, the moral standard proposed has a reflexive element justifying adversary states’ intelligence collection against one another. Therefore, high-tech forms of SIGINT can only be justified at the cost of justifying cruder versions of signals intelligence collection practiced by a state’s less-advanced adversaries. (shrink)

Climate Change is a long-term change in the statistical distribution of weather patterns over periods of time that range from decades to millions of years. It may be a change in the average weather conditions or a change in the distribution of weather events with respect to an average, for example, greater or fewer extreme weather events. It is of keen interest to identify climatological behaviour to discover spatial relationships in climate variables, so that the trend of the climatic changes (...) can be analyzed and studied. Data Mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data. It has also opened up exciting opportunities for exploring and analyzing new types of data and for analyzing old types of data in new ways. As Data Mining is an analytic process designed to explore data in search of consistent patterns and systematic relationships between variables and then to validate the findings by applying the detected patterns to new subsets of data, the present study has been conducted to apply various Data Mining techniques to the climatic data collected and to study the trend, pattern and finally to predict. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine (...) learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Because obituaries are succinct and explicitly intended to summarize their subjects’ lives, they may be expected to include only the features that the author finds most salient but also to signal to others in the community the socially-recognized aspects of the deceased’s character. We begin by reviewing studies 1 and 2, in which obituaries were carefully read and labeled. We then report study 3, which further develops these results with a semi-automated, large-scale semantic analysis of several thousand obituaries. Geography, gender, (...) and elite status all turn out to be associated with the virtues and values associated with the deceased. (shrink)

Customer feedback and reviews are rich sources of information that reflect the sentiments and experiences of consumers, especially in the financial sector. Mining customer sentiments from these textual data sources provides valuable insights for improving services, identifying emerging issues, and predicting customer satisfaction. This paper proposes a novel approach to mining customer sentiments from financial feedback and reviews, leveraging advanced natural language processing (NLP) techniques, sentiment analysis algorithms, and machine learning models. We discuss methods for preprocessing financial (...) text data, feature extraction, sentiment classification, and visualization of sentiment trends. Additionally, we explore the application of sentiment analysis in understanding customer behaviour, improving financial product offerings, and enhancing customer support strategies. Experimental results demonstrate the effectiveness of the proposed methods in accurately predicting customer sentiments and generating actionable insights. This paper also suggests directions for future research, focusing on real-time sentiment monitoring and integration with personalized financial services. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only (...) be extracted from binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

Educational Data Mining (EDM) is a new paradigm aiming to mine and extract knowledge necessary to optimize the effectiveness of teaching process. With normal educational system work it’s often unlikely to accomplish fine system optimizing due to large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational (...) data wherein the effectiveness of Data Mining is explained in migration the educational data into knowledge. The experiments goal at first to identify important factors of teacher behaviors influencing student satisfaction. In addition to presenting experiences gained through the experiments, the paper aims to provide practical guidance of Data Mining solutions in a real application. (shrink)

In this paper we focus on what is referred to as the ‘mineness’ of experience, that is, the intimate familiarity we have with our own thoughts, perceptions, and emotions. Most accounts characterize mineness in terms of an experiential dimension, the first-person givenness of experience, that is subsumed under the notion of minimal self-consciousness or a ‘minimal self’. We argue that this account faces problems and develop an alternative account of mineness in terms of the coherence of experiences with what we (...) label an ‘embodied biography’. Building on a near consensus among consciousness researchers over the function of consciousness as integrating infor- mation, we argue that the phenomenology of mineness consists in the absence of any further thought on top of the experience itself. Finally we argue that this non-phenomenological account of mineness fits well with existing data on pathologies of mineness such as delusions of thought insertion. (shrink)

A huge amount of data is being generated everyday through different transactions in industries, social networking, communication systems etc. Big data is a term that represents vast volumes of high speed, complex and variable data that require advanced procedures and technologies to enable the capture, storage, management, and analysis of the data. Big data analysis is the capacity of representing useful information from these large datasets. Due to characteristics like volume, veracity, and velocity, big (...) class='Hi'>data analysis is becoming one of the most challenging research problems. Semantic analysis is method to better understand the implied or practical meaning of the input dataset. It is mostly applied with ontology to analyze content mainly in web resources. This field of research combines text analysis and Semantic Web technologies. The use semantic knowledge is to aid sentiment analysis of queries like emotion mining, popularity analysis, recommendation systems, user profiling, etc. A new method has been proposed to extract semantic relationships between different data attributes of big data which can be applied to a decision system. (shrink)

Credit scoring is vital for assessing borrowers' creditworthiness and managing risks in financial systems. Traditional credit scoring models often fail to capture non-linear relationships and handle high-dimensional data, leading to less accurate predictions. This research explores the application of advanced data mining algorithms, such as ensemble learning methods, neural networks, and hybrid models, for predicting default rates. Empirical findings reveal significant improvements in predictive accuracy and interpretability. Key takeaways emphasize the importance of effective preprocessing and feature engineering (...) techniques in creating scalable and efficient credit scoring models. (shrink)

Credit scoring is vital for assessing borrowers' creditworthiness and managing risks in financial systems. Traditional credit scoring models often fail to capture non-linear relationships and handle high-dimensional data, leading to less accurate predictions. This research explores the application of advanced data mining algorithms, such as ensemble learning methods, neural networks, and hybrid models, for predicting default rates. Empirical findings reveal significant improvements in predictive accuracy and interpretability. Key takeaways emphasize the importance of effective preprocessing and feature engineering (...) techniques in creating scalable and efficient credit scoring models. (shrink)

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, (...) facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the (...) recent work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

The re-use of publicly available (personal) data for originally unanticipated purposes has become common practice. Without such secondary uses, the development of many AI systems like large language models (LLMs) and ChatGPT would not even have been possible. This chapter addresses the ethical implications of such secondary processing, with a particular focus on data protection and privacy issues. Legal and ethical evaluations of secondary processing of publicly available personal data diverge considerably both among scholars and the general (...) public. While some of these uses are met with opposition and criticism, others are quite unanimously viewed as unproblematic. Often, proponents and opponents of such practices invoke the same ethical and legal standards for their opposite conclusions. This state of affairs shows that other considerations besides the public availability of data must play a role. It calls for a theoretical clarification of the additional criteria that should guide decisions about the (legally informed) ethical acceptability of re-processing practices. In order to make a contribution towards this goal, the present chapter maps the ongoing debate and systematises the existing contributions around three lines of argument: a consent-centred position, an approach that focuses on the distinction between data and information, and finally a line of argument that focuses on the contextual norms that govern the flows of information. The chapter further relates these arguments to three underlying conceptions of privacy and data protection: rights-based, structural and contextual, and discusses the advantages and disadvantages of each position in the light of concrete examples. It concludes by arguing for a mixed approach that combines core elements of the structural and contextual approaches. The chapter aims to contribute to existing research in the fields of data, AI and research ethics, and to reconnect the debate with ethical and legal scholarship on privacy and data protection. In doing so, it aims to make a theoretical contribution towards refining existing conceptions of privacy and data protection in order to make them more fit to ‘drive our digital world’ as far as the use of publicly available data is concerned. (shrink)

Educational Data Mining (EDM) shows interesting scientific results lately. However, little has been discussed about philosophical questions regarding the type of knowledge produced in this area. This paper aims to present two epistemological issues in EDM: (i) a question of ontological nature about the content of the knowledge obtained; and (ii) a question of deontological nature, about the guidelines and principles adopted by the researcher in education, to the detriment of the results of his own research. In the (...) end, some considerations and guidelines are outlined as a result of the discussion of the issues raised. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining qualitative and (...) quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, (...) used for rainfall prediction. In our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

One of the negative legacies of the apartheid era is a markedly skewed mining sector that favours the established companies, and almost completely neglects small-scale mining enterprises. Though a major source of revenue for South Africa(SA), the current state of the mining sector does not directly benefit the previously disadvantaged who dominate small-scale mining. The aim of this study is to explore the support structures and success factors relevant to small scale mining entrepreneurs in South (...) Africa. To achieve this end, the qualitative research approach was utilized to collect and analyze the data. The results affirmed the availability of a comprehensive support structure for aspiring small-scale mining entrepreneurs. A number of factors not limited to having a marketing ethos, requisite business knowledge, possession of appropriate equipment, and the ability to mine effectively and efficiently were reported as being central to successful small-scale mining. Furthermore, it is acknowledged that having passion and the right work ethics worked in favour of successful small-scale entrepreneurs. (shrink)

Abstract: Idea mining is an interesting field in the area of information retrieval and it is increasingly becoming important asset for decision makers. Huge volumes of high quality data from various sources such as scanners, mobile phones, loyalty cards, the web, and social media platforms presents enormous opportunity for organization to achieve success in their businesses. It is possible to achieve this by properly analysing data to reveal feature patterns; hence decision makers can capitalize upon the resulting (...) ideas from wealth of information available. Idea mining helps managers to use their time efficiently by grasping ideas from a huge amount of unstructured data with text mining, instead of reading through page by page, it also helps to avoid the danger of overlooking data with useful ideas. The main objective of this survey article is to provide assessment on a number of successful text mining and text classification tools combined with idea mining measures to extract the idea from texts. (shrink)

The Internet-of-Things (IoT) is gradually being established as the new computing paradigm, which is bound to change the ways of our everyday working and living. IoT emphasizes the interconnection of virtually all types of physical objects (e.g., cell phones, wearables, smart meters, sensors, coffee machines and more) towards enabling them to exchange data and services among themselves, while also interacting with humans as well. Few years following the introduction of the IoT concept, significant hype was generated as a result (...) of the proliferating number of IoT-enabled devices, which (according to many projections) are expected to amount to several billion in the next years. During recent years, this hype has been turning to reality, as a wave of IoT applications with significant social and economic has been emerging. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorization of analytic approaches and propose a layered taxonomy from IoT data to analytics. IoT data analysis is an integral element of any non- trivial IoT system. Nevertheless, IoT analytics are still in their infancy, as IoT data still remain largely unexploited. (shrink)

We present a framework that we are currently developing, that allows one to extract knowledge from the knowledge discovery in database (KDD) dataset. Data mining is a very active and space growing research area. Knowledge discovery in databases (KDD) is very useful in scientific domains. In simple terms, association rule mining is one of the most well-known methods for such knowledge discovery. Initially, database are divided into training and testing for the aid of fuzzy generating the rules (...) using fuzzy rules generation the set of rules are generated from the given dataset. From the generated rules, we are extracting the significant rules by using the improved artificial bee colony algorithm and cuckoo search algorithm (IABCCS). After extracting optimal knowledge from the dataset via rules, the data will be classified using fuzzy classifier with the aid of this finally we will classify the attack and normal. (shrink)

Small-scale mining entrepreneurs are confronted with a variety of challenges during both the start-up and growth phase of their businesses not only in South Africa, but all over the world. Therefore, losing prospects available to them. The aim of this paper was to explore prospects and challenges faced by small scale mining entrepreneurs in South Africa (SA). To attain this end, a qualitative research paradigm was instituted for both data collection and analysis. The findings of this study (...) concur with the literature, that there are indeed plenty of prospects available to small scale mining entrepreneurs. However, the most outstanding prospects was free training and funding. Nonetheless, the receptiveness of these prospects is somehow conditional. -/- . (shrink)

Archaeological data are shadowy in a number of senses. Not only are they notoriously fragmentary but the conceptual and technical scaffolding on which archaeologists rely to constitute these data as evidence can be as constraining as it is enabling. A recurrent theme in internal archaeological debate is that reliance on sedimented layers of interpretative scaffolding carries the risk that “preunderstandings” configure what archaeologists recognize and record as primary data, and how they interpret it as evidence. The selective (...) and destructive nature of data capture in archeology further suggests that there may be little scope for putting “legacy” data to work in new ways. And yet archaeologists have been strikingly successful in mining old datasets for new insights. I situate these concerns in the broader context of debate about the epistemic standing of the historical sciences, and then consider three strategies by which archaeologists address the challenges posed by legacy data. The first two – secondary retrieval and recontextualization – are a matter of reconfiguring the scaffolding that underpins evidential reasoning. The third turns on redeploying old data in the context of computational models that support the experimental simulation of the cultural systems and contexts under study. (shrink)

Climate data analysis performed in order to understand climate change process and effect of different environmental factors in that change, has been focus of interest of researches for many years. Climate change disturbs natural balance, leading to endangerment of many species and their habitats. Data mining techniques provide better and faster analysis of large amounts of data in climatology. Data Mining is a technology that blends traditional data analysis methods with sophisticated algorithms for (...) processing large volumes of data. It has also opened up exciting opportunities for exploring and analyzing new types of data and for analyzing old types of data in new ways. This paper focused on applying few clustering techniques on Climatic data and inferences are studied. Developed solution represents climate data from different points of view in order to provide a complete view of the data for researchers from which they can draw their own conclusions and perform detailed climate data analysis. (shrink)

The art of prediction of stock market volatility has always been a most challenged interdisciplinary research problem among scientist due to its highly non- linear nature of market flow. This paper tries to analysis the historical data of BSE Sensex using extreme volatilities estimators, GARCH, ANN and new proposed Text Mining approach for stock market predictions. Finally experimental results illustrates that the new proposed Text model can able to predict the volatilities of the stock price better than other (...) models. (shrink)

Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such (...) as ontology-based multi-database querying, automated ontology alignment and ontology-based text-mining. These theses are illustrated by means of a case study of the Gene Ontology, a project of increasing importance within the field of biomedical data integration. (shrink)

Abstract— The term Health Informatics represent a huge volume of data that is collected from different source of health sector. Because of its’ diversity in nature, quite a big number of attributes, numerous amount data, health informatics can be considered as Big Data. Therefore, different techniques used for analyzing Big Data will also fit for Health Informatics. In recent years, implementation of Data Mining on Health Informatics brings a lot of fruitful outcomes that improve (...) the overall healthcare system both in analyzing disease and improving healthcare services which eventually reduce expenses. This paper will define the term the health informatics with a detail discussion about different source of heath informatics. Finally, some case study will be illustrated as examples where data mining techniques are applied to produce more efficient, in depth outcomes in analyzing disease. (shrink)

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute (...) to experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

: One of the most active study fields in natural language processing, web mining, and text mining is sentiment analysis. Big data is an important research component in education that is used to advance the value of education by watching students' performance and understanding their learning habits. Real-time student feedback will enable teachers and students to understand teaching and learning challenges in the most user-friendly manner for students. By linking learning analytics to grounded theory, the proposed Deep (...) Convolutional Neural Network (DCNN) analyses students' sentiments and emotions through feedback using a Lexicon-based emotional analysis approach. The sentiment analysis approach is a computer procedure that identifies and classifies subjective information from the source material as good, negative, or neutral. (shrink)

In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more Comprehensive investigation, we develop an approach for revealing, that is, uncovering, (...) Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the “letting the data speak” Correspondence Analysis analytics Platform of Jean-Paul Benzécri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu. (shrink)

Web Log analyser is a tool used for finding the statics of web sites. Through Web Log analyzer the web log files are uploaded into the Hadoop Distributed Framework where parallel procession on log files is carried in the form of master and slave structure. Pig scripts are written on the classified log files to satisfy certain query. The log files are maintained by the web servers. By analysing these log files gives an idea about the user in the way (...) like which IP address have generated the most errors, which user is visiting a web page frequently.. This paper discuss about these log files, their formats, access procedures, their uses, the additional parameters that can be used in the log files which in turn gives way to an effective mining and the tools used to process the log files. It also provides the idea of creating an extended log file and learning the user behaviour. Analysing the user activities is particularly useful for studying user behaviour when using highly interactive systems. This paper presents the details of the methodology used, in which the focus is on studying the information-seeking process and on finding log errors and exceptions. The next part of the paper describes the working and techniques used by web log analyzer. (shrink)