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Intelligent Data Engineering and Automated Learning

Volume 2690 of the series Lecture Notes in Computer Science pp 850-854

Dynamic Subspace Clustering for Very Large High-Dimensional Databases

  • P. Deepa ShenoyAffiliated withUniversity Visvesvaraya College of Engineering
  • , K. G. SrinivasaAffiliated withUniversity Visvesvaraya College of Engineering
  • , M. P. MithunAffiliated withUniversity Visvesvaraya College of Engineering
  • , K. R. VenugopalAffiliated withUniversity Visvesvaraya College of Engineering
  • , L. M. PatnaikAffiliated withMicroprocessor Application Laboratory, Indian Institute of Science

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Abstract

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.