Modern Algorithms of Cluster Analysis

This book provides the reader with a basic understanding of the formal concepts of cluster, clustering, partition, cluster analysis etc.

The book explains feature based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Such an understanding of the formal concepts is particularly vital in the epoch of Big Data, because due to the data volume and characteristics it is no more feasible to rely predominantly on merely viewing the data when facing a clustering problem.

Usually clustering involves choosing similar objects and grouping them together. To facilitate suitable choice of similarity measures for complex and big data, various measures of object similarity, based not only on quantitative (like numerical measurement results) and on qualitative features (like text) as well as on their mixtures, are described, but also graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Various variants, how such similarity measures can be exploited when defining clustering cost functions are presented.

An overview of a number of approaches to handle large collection of objects in reasonable time is provided. Particularly grid-based methods, sampling methods, parallelisation via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially ones used for community detection, are mentioned.

• ISBN: 978-3-319-69307-1
• Editorial: Springer
• Encuadernacion: Cartoné
• Fecha Publicación: 18/02/2018
• Nº Volúmenes: 1
• Idioma: Inglés