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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Xufei, W., & Chunping, L. (2007). DBSC: A dependency-based subspace clustering algorithm for high dimensional numerical datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4830 LNAI, pp. 832–837). https://doi.org/10.1007/978-3-540-76928-6_101
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