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Clustering: A data recovery approachкнига
- Автор: Mirkin B.
- Год издания: 2012
- Издательство: CRC PRESS-TAYLOR & FRANCIS GROUP
- Местоположение издательства: 6000 BROKEN SOUND PARKWAY NW,STE 300, BOCA RATON, USA, FL, 33487-2742
- Объём: 350 страниц (20,0 печатных листов)
- ISBN: 978-1-4398-3841-9
- Монография
- Аннотация: In this book, most popular clustering techniques, K-Means for partitioning, Ward's method for hierarchical clustering, similarity clustering and consensus partitions are presented in the framework of the data recovery approach. This approach leads to the Pythagorean decomposition of the data scatter into parts explained and unexplained by the found cluster structure. The decomposition has led to a number observations that amount to a theoretical framework in clustering. The framework appears to be well suited for extensions of the methods to different data types such as mixed scale data including continuous, nominal and binary features. In addition, a bunch of both conventional and original interpretation aids have been derived for both partitioning and hierarchical clustering based on contributions of features and categories to clusters and splits. One more strain of clustering techniques, one-by-one clustering which is becoming increasingly popular, naturally emerges within the framework to give rise to intelligent versions of K-Means, mitigating the need for user-defined setting of the number of clusters and their hypothetical prototypes. Moreover, the framework leads to a set of mathematically proven properties relating classical clustering with other clustering techniques such as conceptual clustering, spectral clustering, consensus clustering, and graph theoretic clustering as well as with other data analysis concepts such as decision trees and association in contingency data tables.
- Добавил в систему: Миркин Борис Григорьевич