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Granular-relational data mining : how to mine relational data in the paradigm of granular computing? / Piotr Hońko.

Hońko, Piotr, author.
2017
QA76.9.D343
Available Online
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Title
Granular-relational data mining : how to mine relational data in the paradigm of granular computing? / Piotr Hońko.
Author
Hońko, Piotr, author.
ISBN
9783319527512 (electronic book)
3319527517 (electronic book)
9783319527505
3319527509
DOI
https://doi.org/10.1007/978-3-319-52751-2
Published
Cham, Switzerland : Springer, 2017.
Language
English
Description
1 online resource (xv, 123 pages) : illustrations.
Item Number
10.1007/978-3-319-52751-2 doi
Call Number
QA76.9.D343
Dewey Decimal Classification
006.3/12
Summary
This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 10, 2017).
Series
Studies in computational intelligence ; v. 702.
Available in Other Form
Print version: 9783319527505
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Preface
Chapter 1: Introduction
Part I: Generalized Related Set Based Approach
Chapter 2: Information System for Relational Data
Chapter 3: Properties of Granular-Relational Data Mining Framework
Chapter 4: Association Discovery and Classification Rule Mining
Chapter 5: Rough-Granular Computing
Part II: Description Language Based Approach
Chapter 6: Compound Information Systems
Chapter 7: From Granular-Data Mining Framework to its Relational Version
Chapter 8: Relation-Based Granules
Chapter 9: Compound Approximation Spaces
Conclusions
References
Index.

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