Data Mining And Computational Intelligence
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Data Mining With Computational Intelligence
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Author : Lipo Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-08
Data Mining With Computational Intelligence written by Lipo Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-08 with Computers categories.
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.
Data Mining And Computational Intelligence
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Author : Abraham Kandel
language : en
Publisher:
Release Date : 2014-01-15
Data Mining And Computational Intelligence written by Abraham Kandel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Computational Intelligence In Data Mining
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Author : Himansu Sekhar Behera
language : en
Publisher: Springer
Release Date : 2018-07-03
Computational Intelligence In Data Mining written by Himansu Sekhar Behera and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-03 with Computers categories.
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.
Computational Intelligence In Data Mining
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Author : Giacomo Della Riccia
language : en
Publisher: Springer
Release Date : 2014-05-04
Computational Intelligence In Data Mining written by Giacomo Della Riccia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-04 with Computers categories.
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
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Author : Janmenjoy Nayak
language : en
Publisher: Springer Nature
Release Date : 2022-05-06
Computational Intelligence In Data Mining written by Janmenjoy Nayak and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-06 with Technology & Engineering categories.
This book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book is a collection of high-quality peer-reviewed research papers presented in the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021) held at Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India, during December 11–12, 2021. The book addresses the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and 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.
Data Mining With Computational Intelligence
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Author : Wang
language : en
Publisher:
Release Date : 2009-10-01
Data Mining With Computational Intelligence written by Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-01 with categories.
Computational Intelligence In Data Mining
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Author : Himansu Sekhar Behera
language : en
Publisher: Springer
Release Date : 2019-08-18
Computational Intelligence In Data Mining written by Himansu Sekhar Behera and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-18 with Computers categories.
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
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Author : Himansu Sekhar Behera
language : en
Publisher: Springer
Release Date : 2017-05-19
Computational Intelligence In Data Mining written by Himansu Sekhar Behera and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-19 with Technology & Engineering categories.
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.
Data Mining And Computational Intelligence
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Author : Abraham Kandel
language : en
Publisher: Physica
Release Date : 2001-03-13
Data Mining And Computational Intelligence written by Abraham Kandel and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-03-13 with Computers categories.
Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").
Computational Intelligence In Data Mining Volume 2
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Author : Lakhmi C. Jain
language : en
Publisher: Springer
Release Date : 2014-12-10
Computational Intelligence In Data Mining Volume 2 written by Lakhmi C. Jain and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-10 with Technology & Engineering categories.
The contributed volume aims to explicate and address the difficulties and challenges that of seamless integration of the 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.