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ABSTRACT

Subspace clustering algorithms in their most general form attempt to describe data with clusters that are not constrained to index a common set of attributes. Previous evolutionary approaches to this problem have assumed a weaker model in which clusters are built in a common subset. Moreover, a filter method is generally assumed in which a classical clustering algorithm is employed in the inner loop. Needless to say, this presents a considerable computational overhead. In this work we recognize the utility of assuming a `bottom-up' approach to subspace clustering. Specifically, we apply a classical clustering algorithm to each attribute to establish 1-d clusters that are then indexed by a MOGA to design a population of subspace clusters. The ensuing search is entirely in terms of a combinatorial optimization problem, thus computationally very efficient. A final single objective GA is then applied to search the set of subspace clusters identified under the MOGA for the most suitable combination.

REFERENCES

• 1: YeongSeog Kim , W. Nick Street , Filippo Menczer, Feature selection in unsupervised learning via evolutionary search, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.365-369, August 20-23, 2000, Boston, Massachusetts, United States [doi>10.1145/347090.347169]
• 2: Lance Parsons , Ehtesham Haque , Huan Liu, Subspace clustering for high dimensional data: a review, ACM SIGKDD Explorations Newsletter, v.6 n.1, p.90-105, June 2004 [doi>10.1145/1007730.1007731]
• 3: Dan Pelleg , Andrew W. Moore, X-means: Extending K-means with Efficient Estimation of the Number of Clusters, Proceedings of the Seventeenth International Conference on Machine Learning, p.727-734, June 29-July 02, 2000

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Parameter learning

General Terms:
Algorithms, Experimentation, Performance

Keywords:
multi-objective genetic algorithm, subspace clustering