Book: Data Clustering: Algorithms and Applications

The chapters are carefully constructed to cover the area of clustering comprehensively with up-to-date surveys, making this book accessible to beginning data scientists and analysts.

Data Clustering: Algorithms and ApplicationBook Announcement: Data Clustering: Algorithms and Applications
Editors: Charu C. Aggarwal and Chandan K.Reddy

CRC Press, September 2013
Approximately 620 pages

This is an edited book on data clustering.The set of chapters, the individual authors and the material in each chapters are carefully constructed so as to cover the area of clustering comprehensively with up-to-date surveys.Each chapter contains carefully organized material, which includes introductory material as well as advanced material from the recent literature. Thus, the book can also be used as a reference aid. Emphasis was placed on simplifying the content, so that the material is relatively easy to assimilate. The book assumes relatively little prior background, other than a basic understanding of probability and statistical concepts.Therefore, in spite of its deep coverage, it can also provide a good introduction to the beginner.

Chapters cover one of three areas:

(i) Methods and techniques commonly used in data clustering,such as feature selection issues, probabilistic methods, distance-based methods, density-based methods, grid-based methods,non-negative matrix
factorization, and spectral methods.In addition, special chapters are devoted
to scalability andbig data issues, streaming data, and high dimensional data.

(ii) data domains, such as, text, categorical, mixed-attribute, time-series, discrete sequence, uncertain and network data;

(iii) key enhancements of cluster analysis with semi-supervision, human feedback, multi-view methods, and ways of measuring effectiveness with validation techniques.

The table of contents and introductory chapter may be found at: