Shenoy, Deepa P and Srinivasa, KG and Mithun, MP and Venugopal, KR and Patnaik, LM (2003) Dynamic subspace clustering for very large high-dimensional databases. In: Lecture Notes in Computer Science, 2690 . pp. 850-854.
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Emerging high-dimensional data mining applications needs to find interesting clusters embeded in arbitrarily aligned subspaces of lower dimensionality. It is difficult to cluster high-dimensional data objects, when they are sparse and skewed. Updations are quite common in dynamic databases and they are usually processed in batch mode. In very large dynamic databases, it is necessary to perform incremental cluster analysis only to the updations. We present a incremental clustering algorithm for subspace clustering in very high dimensions, which handles both insertion and deletions of datapoints to the backend databases.
|Item Type:||Editorials/Short Communications|
|Additional Information:||Copyright of this article belongs to Springer.|
|Date Deposited:||19 Aug 2011 07:09|
|Last Modified:||19 Aug 2011 07:09|
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