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.
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© 2007 Springer-Verlag Berlin Heidelberg
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Wang, X., Li, C. (2007). DBSC: A Dependency-Based Subspace Clustering Algorithm for High Dimensional Numerical Datasets. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_101
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DOI: https://doi.org/10.1007/978-3-540-76928-6_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76926-2
Online ISBN: 978-3-540-76928-6
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