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Modern Algorithms of Cluster Analysis (Hardcover, 1st ed. 2018)
Loot Price: R5,328
Discovery Miles 53 280
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Modern Algorithms of Cluster Analysis (Hardcover, 1st ed. 2018)
Series: Studies in Big Data, 34
Expected to ship within 10 - 15 working days
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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.
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