Clustering High Dimensional Massive Scientific Datasets

  • Authors:

  • Ekow J. Otoo;Arie Shoshani;Seung-Won Hwang

  • Affiliations:

  • Lawrence Berkeley National Laboratory, 1 Cyclotron Road, University of California, Berkeley, CA 94720, USA. ejotoo@lbl.gov;Lawrence Berkeley National Laboratory, 1 Cyclotron Road, University of California, Berkeley, CA 94720, USA. shoshani@lbl.gov;Department of Computer Science, University of Illinois at Urbana-Champaign, 1304 W. Springfield Avenue, Urbana, IL 61801, USA

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2001

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Abstract

Many scientific applications can benefit from an efficient clustering algorithm of massively large high dimensional datasets. However most of the developed algorithms are impractical to use when the amount of data is very large. Given N objects each defined by an M-dimensional feature vector, any clustering technique for handling very large datasets in high dimensional space should run in time O(MN) at best, and O(MN log N) in the worst case, using no more than O(MN) storage, for it to be practical. We introduce a hybrid algorithm, called HyCeltyc, for clustering massively large high dimensional datasets in O(MN) time which is linear in the size of the data. HyCeltyc, which stands for Hybrid Cell Density Clustering method, combines a cell-density based algorithm with a hierarchical agglomerative method to identify clusters in linear time. The main steps of the algorithm involve sampling, dimensionality reduction, selection of significant features on which to cluster the data and a grid-based clustering algorihm that is linear in the data size.