Computational Intelligence in Data Mining - Volume 3

Computational Intelligence in Data Mining - Volume 3

Author: Lakhmi C. Jain

Publisher: Springer

Published: 2014-12-11

Total Pages: 716

ISBN-13: 8132222024

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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

Computational Intelligence in Data Mining

Author: Giacomo Della Riccia

Publisher: Springer

Published: 2014-05-04

Total Pages: 169

ISBN-13: 370912588X

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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.


Data Mining with Computational Intelligence

Data Mining with Computational Intelligence

Author: Lipo Wang

Publisher: Springer Science & Business Media

Published: 2005-12-08

Total Pages: 280

ISBN-13: 3540288031

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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

Computational Intelligence in Data Mining

Author: Himansu Sekhar Behera

Publisher: Springer

Published: 2017-05-19

Total Pages: 825

ISBN-13: 9811038740

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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.


Computational Intelligence in Data Mining

Computational Intelligence in Data Mining

Author: Himansu Sekhar Behera

Publisher: Springer

Published: 2019-08-17

Total Pages: 789

ISBN-13: 9811386765

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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.


Nature-Inspired Computation in Data Mining and Machine Learning

Nature-Inspired Computation in Data Mining and Machine Learning

Author: Xin-She Yang

Publisher: Springer Nature

Published: 2019-09-03

Total Pages: 282

ISBN-13: 3030285537

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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.


Intelligent Data Mining

Intelligent Data Mining

Author: Da Ruan

Publisher: Springer Science & Business Media

Published: 2005-08-24

Total Pages: 536

ISBN-13: 9783540262565

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"Intelligent Data Mining – Techniques and Applications" is an organized edited collection of contributed chapters covering basic knowledge for intelligent systems and data mining, applications in economic and management, industrial engineering and other related industrial applications. The main objective of this book is to gather a number of peer-reviewed high quality contributions in the relevant topic areas. The focus is especially on those chapters that provide theoretical/analytical solutions to the problems of real interest in intelligent techniques possibly combined with other traditional tools, for data mining and the corresponding applications to engineers and managers of different industrial sectors. Academic and applied researchers and research students working on data mining can also directly benefit from this book.


Data Mining

Data Mining

Author: Florin Gorunescu

Publisher: Springer Science & Business Media

Published: 2011-03-10

Total Pages: 364

ISBN-13: 3642197213

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The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.


Rough – Granular Computing in Knowledge Discovery and Data Mining

Rough – Granular Computing in Knowledge Discovery and Data Mining

Author: J. Stepaniuk

Publisher: Springer

Published: 2009-01-29

Total Pages: 162

ISBN-13: 3540708014

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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.


Emerging Technologies in Data Mining and Information Security

Emerging Technologies in Data Mining and Information Security

Author: João Manuel R. S. Tavares

Publisher: Springer Nature

Published: 2021-05-04

Total Pages: 994

ISBN-13: 981159774X

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This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of things (IoT), and information security.