DBSC: a dependency-based subspace clustering algorithm for high dimensional numerical datasets

Authors:
Xufei Wang;Chunping Li
Affiliations:
School of Software, Tsinghua University, China MOE Key Laboratory for Information System Security;School of Software, Tsinghua University, China MOE Key Laboratory for Information System Security
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

We present a novel algorithm called DBSC, which finds subspace clusters in numerical datasets based on the concept of "dependency". This algorithm uses a depth-first search strategy to find out the maximal subspaces: a new dimension is added to current k-subspace and its validity as a (k+1)-subspace is evaluated. The clusters within those maximal subspaces are mined in a similar fashion as maximal subspace mining does. With the experiments on synthetic and real datasets, our algorithm is shown to be both effective and efficient for high dimensional datasets.