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OverviewClustering 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. Full Product DetailsAuthor: Jiwu ZhaoPublisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG Imprint: Sudwestdeutscher Verlag Fur Hochschulschriften AG Dimensions: Width: 15.20cm , Height: 0.90cm , Length: 22.90cm Weight: 0.222kg ISBN: 9783838138305ISBN 10: 3838138309 Pages: 144 Publication Date: 26 March 2014 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: In stock We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |