Last edited by Tudal
Wednesday, May 6, 2020 | History

3 edition of Rough – Granular Computing in Knowledge Discovery and Data Mining found in the catalog.

Rough – Granular Computing in Knowledge Discovery and Data Mining

by JarosЕ‚aw Stepaniuk

  • 349 Want to read
  • 39 Currently reading

Published by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English

    Subjects:
  • Engineering mathematics,
  • Artificial intelligence

  • Edition Notes

    Statementby Jarosław Stepaniuk ; edited by Janusz Kacprzyk
    SeriesStudies in Computational Intelligence -- 152
    ContributionsKacprzyk, Janusz, SpringerLink (Online service)
    The Physical Object
    Format[electronic resource] /
    ID Numbers
    Open LibraryOL25553120M
    ISBN 109783540708001, 9783540708018

    This volume contains the papers selected for presentation at the 10th Int- national Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC , organized at the University of Regina, August 31st&#;September 3rd, This conference followed in the footsteps of Brand: Dominik Slezak. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human 5/5(1).

    Rough Sets, Fuzzy Sets, Data Mining and Granular Computing by Sakai. Title Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. The 56 revised full papers presented together with 6 invited papers and a report on the Rough Set Year in India project were carefully reviewed and selected from a total of submissions.   Granular computing and soft computing have begun to play important roles in bioinformatics, e-business, security, machine learning, data mining, high-performance computing and wireless mobile computing in terms of efficiency, effectiveness, robustness and uncertainty.

    He is the author of over 10 books, the editor of dozens of proceedings of international and national conferences, and has more than reviewed research publications. His research interests include rough set, granular computing, knowledge technology, data mining, neural network, and cognitive computing. He is a senior member of the by: Several approaches for granular computing are suggested using fuzzy set theory, rough set theory, power algebras and interval analysis. The rough set theoretic approach is based on the principles of set approximation and provides an attractive framework for data mining and knowledge discovery.


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Rough – Granular Computing in Knowledge Discovery and Data Mining by JarosЕ‚aw Stepaniuk Download PDF EPUB FB2

The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough. The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough Format: Hardcover.

"The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough. Stepaniuk J. () Mining Knowledge from Complex Data.

In: Rough – Granular Computing in Knowledge Discovery and Data Mining. Studies in Computational Intelligence, vol Author: Jarosław Stepaniuk. Granular Computing and Rough Sets. /X_ In book: Data Mining and Knowledge Discovery Handbook (pp) To the best of. This book constitutes the refereed proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrCheld in Delhi, India in December in conjunction with the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI In this perspective, granular computing has a position of centrality in data mining.

Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing.

Yao Y, Miao D, Zhang N and Xu F Set-theoretic models of granular structures Proceedings of the 5th international conference on Rough set and knowledge technology, () Yager R () Participatory learning with granular observations, IEEE Transactions on Fuzzy Systems,(), Online publication date: 1-Feb The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely Author: Jianchao Han.

Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology by: 6.

Granular computing (GrC) is an emerging computing paradigm of information processing that concerns the processing of complex information entities called "information granules", which arise in the process of data abstraction and derivation of knowledge from information or data.

Generally speaking, information granules are collections of entities that usually originate at the numeric level and. 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.

Category: Computers Human Centric Information Processing Through Granular Modelling. Witold Pedrycz is a Professor and Canada Research Chair (CRC) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.

He is also with the Systems Research Institute of the Polish Academy of Sciences. He is actively pursuing research in Computational Intelligence, fuzzy modeling, knowledge discovery and data mining, fuzzy control.

Description. Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning.

The book “Rough–Granular Computing in Knowledge Discovery and Data Mining” written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing ar-eas: granular computing, rough.

This book is about Granular Computing (GC) - an emerging conceptual and of information processing. As the name suggests, GC concerns computing paradigm processing of complex information entities - information granules.

In essence, information granules arise in the process of abstraction of data and derivation of knowledge from information. Yao, Y. and Zhong, N. Potential applications of granular computing in knowledge discovery and data mining, Proceedings of World Multiconference on Systemics, Cybernetics and Informatics, Volume 5,]] Google Scholar; Read "Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing 15th International Conference, RSFDGrCTianjin, China, November, Proceedings" by available from Rakuten Kobo.

This book constitutes the refereed conference proceedings of Brand: Springer International Publishing. This book constitutes the refereed proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrCheld in Toronto, Canada in May in conjunction with the Second International Conference on Rough Sets and Knowledge Technology, RSKTboth as part of the Joint Rough Set Symposium, JRS Author: Aijun An.

Additional studies of granular computing, within the context of computational intelligence, can be found in recently edited books,22,39,51 a book by Bargiela and Pedrycz,4 and conferences proceedings of International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing and IEEE International Conference on Granular Computing.

Discovery of Process Models from Data and Domain Knowledge: A Rough-Granular Approach: /ch The rapid expansion of the Internet has resulted not only in the ever-growing amount of data stored therein, but also in the burgeoning complexity of theCited by: In this chapter, we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications.

Since a major focus of this book is on rough sets and its applications to database mining, one full section is .Summary on KDD and data mining Knowledge discovery in databases is the process of identifying valid, novel, potentially useful, and ultimately understandable patterns/models in data.

Data mining is a step in the knowledge discovery process consisting of particular data File Size: KB.