Overview
- Provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, and cluster analysis
- Presents a number of approaches to handling a large number of objects within a reasonable time
- Presents recent research on cluster analysis
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 34)
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Table of contents (7 chapters)
Keywords
About this book
This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.
The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem.
Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented.
In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
Authors and Affiliations
Bibliographic Information
Book Title: Modern Algorithms of Cluster Analysis
Authors: Slawomir Wierzchoń, Mieczyslaw Kłopotek
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-69308-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-69307-1Published: 29 January 2018
Softcover ISBN: 978-3-319-88752-4Published: 04 June 2019
eBook ISBN: 978-3-319-69308-8Published: 29 December 2017
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XX, 421
Number of Illustrations: 51 b/w illustrations
Topics: Computational Intelligence, Big Data, Applications of Mathematics, Big Data/Analytics