Charu C. Aggarwal
ABSTRACT
High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Therefore, techniques have recently been proposed to find clusters in hidden subspaces of the data. However, since the behavior of the data may vary considerably in different subspaces, it is often difficult to define the notion of a cluster with the use of simple mathematical formalizations. In fact, the meaningfulness and definition of a cluster is best characterized with the use of human intuition. In this paper, we propose a system which performs high dimensional clustering by effective cooperation between the human and the computer. The complex task of cluster creation is accomplished by a combination of human intuition and the computational support provided by the computer. The result is a system which leverages the best abilities of both the human and the computer in order to create very meaningful sets of clusters in high dimensionality.
- 1.C. C. Aggarwal. Re-designing distance functions and distance based applications for high dimensional data. ACM SIGMOD Record, March 2001. Google Scholar
Digital Library
- 2.C. C. Aggarwal. A Human-Computer Cooperative System for Effective High Dimensional Clustering, IBM Research Report, 2001.Google ScholarDigital Library
- 3.C. C. Aggarwal et al. Fast algorithms for projected clustering. A CM SIGMOD Conference, 1999. Google ScholarDigital Library
- 4.C. C. Aggarwal, P. S. Yu. Finding Generalized Projected Clusters in High Dimensional Spaces. ACM SIGMOD Conference, 2000. Google ScholarDigital Library
- 5.R. Srikant, R. Agrawal. Mining Quantitative Association Rules in Large Relational Tables. A CM SIGMOD Conference, 1996. Google ScholarDigital Library
- 6.It. Agrawal et al. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. ACM SIGMOD Conference, 1998. Google ScholarDigital Library
- 7.A. Hinneburg, D. A. Keim, M. Wawryniuk. HD-Eye: Visual Mining of High Dimensional Data. IEEE Comp. Graphics and Applications, 19(5), pp. 22-31, 1999. Google ScholarDigital Library
- 8.I. T. Jolliffe. Principal Component Analysis, Springer-Verlag, New York, 1986.Google ScholarCross Ref
Index Terms
- A human-computer cooperative system for effective high dimensional clustering
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