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Automatic Subspace Clustering

Autor Jiwu Zhao
en Limba Engleză Paperback – 26 mar 2014
Clustering is an important task of data mining. The traditional clustering approaches are designed for searching clusters in the entire space. However, there are usually many irrelevant attributes for clustering in high-dimensional data sets, where the traditional clustering methods often work improperly. Subspace clustering is an extension of traditional clustering that enables finding subspace clusters only in relevant dimensions within a data set. Most subspace clustering methods usually suffer from the issue that their complicated parameter settings are almost troublesome to be determined, and therefore it can be difficult to implement these methods in practical applications. In this book, we introduce two novel subspace clustering methods SUGRA and ASCDD. Both of them are designed with the principle of uncomplicated parameter setting and easy applicability.
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Specificații

ISBN-13: 9783838138305
ISBN-10: 3838138309
Pagini: 144
Dimensiuni: 150 x 220 x 10 mm
Greutate: 0.23 kg
Editura: Südwestdeutscher Verlag für Hochschulschriften

Notă biografică

Jiwu Zhao, Dr.rer.nat.: received his doctor's degree at the Heinrich Heine University Düsseldorf in the Institute of Computer Science and the Department of Databases and Information Systems.