Download or read book Mathematical Methods for Knowledge Discovery and Data Mining written by Felici, Giovanni and published by IGI Global. This book was released on 2007-10-31 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance, manufacturing, marketing, performance measurement, and telecommunications"--Provided by publisher.

Download or read book Data Mining Methods for Knowledge Discovery written by Krzysztof J. Cios and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Download or read book Data Mining written by Krzysztof J. Cios and published by Springer Science & Business Media. This book was released on 2007-10-05 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Download or read book Data Mining and Knowledge Discovery via Logic Based Methods written by Evangelos Triantaphyllou and published by Springer Science & Business Media. This book was released on 2010-06-08 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.

Download or read book Knowledge Discovery with Support Vector Machines written by Lutz H. Hamel and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.

Download or read book Rough Granular Computing in Knowledge Discovery and Data Mining written by J. Stepaniuk and published by Springer. This book was released on 2009-01-29 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers methods based on a combination of granular computing, rough sets, and knowledge discovery in data mining (KDD). The discussion of KDD foundations based on the rough set approach and granular computing feature illustrative applications.

Download or read book Scientific Data Mining and Knowledge Discovery written by Mohamed Medhat Gaber and published by Springer Science & Business Media. This book was released on 2009-09-19 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.

Download or read book Knowledge Discovery and Measures of Interest written by Robert J. Hilderman and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity measures are used to rank summaries generated from databases. The knowledge discovery practitioner will find solid empirical evidence on which to base decisions regarding the choice of measures in data mining applications. The knowledge discovery student in a senior undergraduate or graduate course in databases and data mining will find the book is a good introduction to the concepts and techniques of measures of interest. In Knowledge Discovery and Measures of Interest, we study two closely related steps in any knowledge discovery system: the generation of discovered knowledge; and the interpretation and evaluation of discovered knowledge. In the generation step, we study data summarization, where a single dataset can be generalized in many different ways and to many different levels of granularity according to domain generalization graphs. In the interpretation and evaluation step, we study diversity measures as heuristics for ranking the interestingness of the summaries generated. The objective of this work is to introduce and evaluate a technique for ranking the interestingness of discovered patterns in data. It consists of four primary goals: To introduce domain generalization graphs for describing and guiding the generation of summaries from databases. To introduce and evaluate serial and parallel algorithms that traverse the domain generalization space described by the domain generalization graphs. To introduce and evaluate diversity measures as heuristic measures of interestingness for ranking summaries generated from databases. To develop the preliminary foundation for a theory of interestingness within the context of ranking summaries generated from databases. Knowledge Discovery and Measures of Interest is suitable as a secondary text in a graduate level course and as a reference for researchers and practitioners in industry.

Download or read book Foundations of Data Mining and Knowledge Discovery written by Tsau Young Lin and published by Springer Science & Business Media. This book was released on 2005-09-02 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.

Download or read book Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques written by Evangelos Triantaphyllou and published by Springer Science & Business Media. This book was released on 2006-09-10 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book outlines the core theory and practice of data mining and knowledge discovery (DM & KD) examining theoretical foundations for various methods, and presenting an array of examples, many drawn from real-life applications. Most theoretical developments are accompanied by extensive empirical analysis, offering a deep insight into both theoretical and practical aspects of the subject. The book presents the combined research experiences of 40 expert contributors of world renown.

Download or read book Data Science Learning by Latent Structures and Knowledge Discovery written by Berthold Lausen and published by Springer. This book was released on 2015-05-06 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering and pattern recognition methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking and finance; engineering; geography and geology; archeology, sociology, educational sciences, linguistics and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.

Download or read book Advances in Knowledge Discovery and Data Mining written by Honghua Dai and published by Springer. This book was released on 2004-04-22 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. This year, the eighth in the series (PAKDD 2004) was held at Carlton Crest Hotel, Sydney, Australia, 26–28 May 2004. PAKDD is a leading international conference in the area of data mining. It p- vides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. The selection process this year was extremely competitive. We received 238 researchpapersfrom23countries,whichisthehighestinthehistoryofPAKDD, and re?ects the recognition of and interest in this conference. Each submitted research paper was reviewed by three members of the program committee. F- lowing this independent review, there were discussions among the reviewers, and when necessary, additional reviews from other experts were requested. A total of 50 papers were selected as full papers (21%), and another 31 were selected as short papers (13%), yielding a combined acceptance rate of approximately 34%. The conference accommodated both research papers presenting original - vestigation results and industrial papers reporting real data mining applications andsystemdevelopmentexperience.Theconferencealsoincludedthreetutorials on key technologies of knowledge discovery and data mining, and one workshop focusing on speci?c new challenges and emerging issues of knowledge discovery anddatamining.ThePAKDD2004programwasfurtherenhancedwithkeynote speeches by two outstanding researchers in the area of knowledge discovery and data mining: Philip Yu, Manager of Software Tools and Techniques, IBM T.J.

Download or read book Advances in Knowledge Discovery and Data Mining written by Takashi Washio and published by Springer. This book was released on 2008-05-11 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. PAKDD 2008, the 12th in the series, was heldatOsaka,JapanduringMay20–23,2008.PAKDDisaleadinginternational conference in the area of data mining. It provides an international forum for - searchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD-related areas - cluding data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. This year we received a total of 312 research papers from 34 countries and regions in Asia, Australia, North America, South America, Europe, and Africa. Every submitted paper was rigorously reviewed by two or three reviewers, d- cussed by the reviewers under the supervision of an Area Chair, and judged by the Program Committee Chairs. When there was a disagreement, the Area Chair and/or the Program Committee Chairs provided an additional review. Thus, many submissions were reviewed by four experts. The Program Comm- tee members were deeply involved in a highly selective process. As a result, only approximately11.9%ofthe312submissionswereacceptedaslongpapers,12.8% of them were accepted as regular papers, and 11.5% of them were accepted as short papers.

Download or read book Advances in Knowledge Discovery and Data Mining written by Wee Keong Ng and published by Springer. This book was released on 2006-03-10 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.

Download or read book Soft Computing for Knowledge Discovery and Data Mining written by Oded Maimon and published by Springer Science & Business Media. This book was released on 2007-10-25 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.

Download or read book Knowledge Discovery and Data Mining written by O. Maimon and published by Springer Science & Business Media. This book was released on 2000-12-31 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).

Download or read book Mathematical Tools for Data Mining written by Dan A. Simovici and published by Springer Science & Business Media. This book was released on 2008-08-15 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume was born from the experience of the authors as researchers and educators,whichsuggeststhatmanystudentsofdataminingarehandicapped in their research by the lack of a formal, systematic education in its mat- matics. The data mining literature contains many excellent titles that address the needs of users with a variety of interests ranging from decision making to p- tern investigation in biological data. However, these books do not deal with the mathematical tools that are currently needed by data mining researchers and doctoral students. We felt it timely to produce a book that integrates the mathematics of data mining with its applications. We emphasize that this book is about mathematical tools for data mining and not about data mining itself; despite this, a substantial amount of applications of mathematical c- cepts in data mining are presented. The book is intended as a reference for the working data miner. In our opinion, three areas of mathematics are vital for data mining: set theory,includingpartially orderedsetsandcombinatorics;linear algebra,with its many applications in principal component analysis and neural networks; and probability theory, which plays a foundational role in statistics, machine learning and data mining. Thisvolumeisdedicatedtothestudyofset-theoreticalfoundationsofdata mining. Two further volumes are contemplated that will cover linear algebra and probability theory. The ?rst part of this book, dedicated to set theory, begins with a study of functionsandrelations.Applicationsofthesefundamentalconceptstosuch- sues as equivalences and partitions are discussed. Also, we prepare the ground for the following volumes by discussing indicator functions, ?elds and?-?elds, and other concepts.