Computational Intelligence In Data Mining Volume 3

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Data Mining with Computational Intelligence

Author : Lipo Wang
Publisher : Springer Science & Business Media
ISBN 13 : 3540288031
Total Pages : 280 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Data Mining with Computational Intelligence by : Lipo Wang

Download or read book Data Mining with Computational Intelligence written by Lipo Wang and published by Springer Science & Business Media. This book was released on 2005-12-08 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.

Computational Intelligence in Data Mining

Author : Giacomo Della Riccia
Publisher : Springer
ISBN 13 : 370912588X
Total Pages : 169 pages
Book Rating : 4.7/5 (91 download)

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Book Synopsis Computational Intelligence in Data Mining by : Giacomo Della Riccia

Download or read book Computational Intelligence in Data Mining written by Giacomo Della Riccia and published by Springer. This book was released on 2014-05-04 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases” the book starts with a unified view on ‘Data Mining and Statistics – A System Point of View’. Two special techniques follow: ‘Subgroup Mining’, and ‘Data Mining with Possibilistic Graphical Models’. "Data Fusion and Possibilistic or Fuzzy Data Analysis” is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on learning of fuzzy models is studied. In the domain of "Classification and Decomposition” adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section "Learning and Data Fusion” learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models.

Computational Intelligence in Data Mining - Volume 3

Author : Lakhmi C. Jain
Publisher : Springer
ISBN 13 : 8132222024
Total Pages : 717 pages
Book Rating : 4.1/5 (322 download)

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Book Synopsis Computational Intelligence in Data Mining - Volume 3 by : Lakhmi C. Jain

Download or read book Computational Intelligence in Data Mining - Volume 3 written by Lakhmi C. Jain and published by Springer. This book was released on 2014-12-11 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributed volume aims to explicate and address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Computational Intelligence in Data Mining

Author : Himansu Sekhar Behera
Publisher : Springer
ISBN 13 : 9811038740
Total Pages : 847 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Computational Intelligence in Data Mining by : Himansu Sekhar Behera

Download or read book Computational Intelligence in Data Mining written by Himansu Sekhar Behera and published by Springer. This book was released on 2017-05-19 with total page 847 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science.

Introduction to Data Mining and its Applications

Author : S. Sumathi
Publisher : Springer
ISBN 13 : 3540343512
Total Pages : 828 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Introduction to Data Mining and its Applications by : S. Sumathi

Download or read book Introduction to Data Mining and its Applications written by S. Sumathi and published by Springer. This book was released on 2006-10-12 with total page 828 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.

Computational Intelligence in Data Mining

Author : Himansu Sekhar Behera
Publisher : Springer
ISBN 13 : 9811386765
Total Pages : 801 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Computational Intelligence in Data Mining by : Himansu Sekhar Behera

Download or read book Computational Intelligence in Data Mining written by Himansu Sekhar Behera and published by Springer. This book was released on 2019-08-17 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceeding discuss the latest solutions, scientific findings and methods for solving intriguing problems in the fields of data mining, computational intelligence, big data analytics, and soft computing. This gathers outstanding papers from the fifth International Conference on “Computational Intelligence in Data Mining” (ICCIDM), and offer a “sneak preview” of the strengths and weaknesses of trending applications, together with exciting advances in computational intelligence, data mining, and related fields.

Computational Intelligence in Data Mining—Volume 1

Author : Himansu Sekhar Behera
Publisher : Springer
ISBN 13 : 8132227344
Total Pages : 494 pages
Book Rating : 4.1/5 (322 download)

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Book Synopsis Computational Intelligence in Data Mining—Volume 1 by : Himansu Sekhar Behera

Download or read book Computational Intelligence in Data Mining—Volume 1 written by Himansu Sekhar Behera and published by Springer. This book was released on 2015-12-08 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on Computational Intelligence in Data Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India during 5 – 6 December 2015. The two-volume Proceedings address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Foundations of Computational Intelligence

Author : Ajith Abraham
Publisher : Springer Science & Business Media
ISBN 13 : 3642010903
Total Pages : 397 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Foundations of Computational Intelligence by : Ajith Abraham

Download or read book Foundations of Computational Intelligence written by Ajith Abraham and published by Springer Science & Business Media. This book was released on 2009-04-27 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Computational Intelligence Volume 6: Data Mining: Theoretical Foundations and Applications Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; arti- cial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are - plied to Data Mining problems. Computational tools or solutions based on intel- gent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated. This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Int- ligence techniques for Data Mining. The book is divided into 3 parts: Part-I: Data Click Streams and Temporal Data Mining Part-II: Text and Rule Mining Part-III: Applications Part I on Data Click Streams and Temporal Data Mining contains four chapters that describe several approaches in Data Click Streams and Temporal Data Mining.

Computational Intelligence in Data Mining

Author : Giacomo Della Riccia
Publisher :
ISBN 13 : 9783709125892
Total Pages : 176 pages
Book Rating : 4.1/5 (258 download)

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Book Synopsis Computational Intelligence in Data Mining by : Giacomo Della Riccia

Download or read book Computational Intelligence in Data Mining written by Giacomo Della Riccia and published by . This book was released on 2014-09-01 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Intelligence in Data Mining - Volume 1

Author : Lakhmi C. Jain
Publisher : Springer
ISBN 13 : 8132222059
Total Pages : 713 pages
Book Rating : 4.1/5 (322 download)

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Book Synopsis Computational Intelligence in Data Mining - Volume 1 by : Lakhmi C. Jain

Download or read book Computational Intelligence in Data Mining - Volume 1 written by Lakhmi C. Jain and published by Springer. This book was released on 2014-12-10 with total page 713 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributed volume aims to explicate and address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Computational Intelligence in Data Mining

Author : Himansu Sekhar Behera
Publisher : Springer
ISBN 13 : 9811080550
Total Pages : 896 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Computational Intelligence in Data Mining by : Himansu Sekhar Behera

Download or read book Computational Intelligence in Data Mining written by Himansu Sekhar Behera and published by Springer. This book was released on 2018-07-03 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Conference on “Computational Intelligence in Data Mining” (ICCIDM), after three successful versions, has reached to its fourth version with a lot of aspiration. The best selected conference papers are reviewed and compiled to form this volume. The proceedings discusses the latest solutions, scientific results and methods in solving intriguing problems in the fields of data mining, computational intelligence, big data analytics, and soft computing. The volume presents a sneak preview into the strengths and weakness of trending applications and research findings in the field of computational intelligence and data mining along with related field.

Data Mining With Computational Intelligence

Author : Wang
Publisher :
ISBN 13 : 9788184893588
Total Pages : 288 pages
Book Rating : 4.8/5 (935 download)

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Book Synopsis Data Mining With Computational Intelligence by : Wang

Download or read book Data Mining With Computational Intelligence written by Wang and published by . This book was released on 2009-10-01 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning and Data Mining

Author : Igor Kononenko
Publisher : Elsevier
ISBN 13 : 0857099442
Total Pages : 480 pages
Book Rating : 4.8/5 (57 download)

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Book Synopsis Machine Learning and Data Mining by : Igor Kononenko

Download or read book Machine Learning and Data Mining written by Igor Kononenko and published by Elsevier. This book was released on 2007-04-30 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining. Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions. Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining A valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions

Soft Computing for Data Mining Applications

Author : K. R. Venugopal
Publisher : Springer
ISBN 13 : 3642001939
Total Pages : 341 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Soft Computing for Data Mining Applications by : K. R. Venugopal

Download or read book Soft Computing for Data Mining Applications written by K. R. Venugopal and published by Springer. This book was released on 2009-02-24 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However,the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.

Pocket Data Mining

Author : Mohamed Medhat Gaber
Publisher : Springer Science & Business Media
ISBN 13 : 3319027115
Total Pages : 108 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Pocket Data Mining by : Mohamed Medhat Gaber

Download or read book Pocket Data Mining written by Mohamed Medhat Gaber and published by Springer Science & Business Media. This book was released on 2013-10-19 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Nature-Inspired Computation in Data Mining and Machine Learning

Author : Xin-She Yang
Publisher : Springer Nature
ISBN 13 : 3030285537
Total Pages : 273 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Nature-Inspired Computation in Data Mining and Machine Learning by : Xin-She Yang

Download or read book Nature-Inspired Computation in Data Mining and Machine Learning written by Xin-She Yang and published by Springer Nature. This book was released on 2019-09-03 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.

Data Preprocessing in Data Mining

Author : Salvador García
Publisher : Springer
ISBN 13 : 3319102478
Total Pages : 320 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Data Preprocessing in Data Mining by : Salvador García

Download or read book Data Preprocessing in Data Mining written by Salvador García and published by Springer. This book was released on 2014-08-30 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.