Abstract
High-dimensional data indexing and query is a challenging problem due to the inherent sparsity of the data. Fast algorithms are in an urgent need in this field. In this paper, an automatic subspace dimension selection (ASDS) based clustering algorithm is derived from the well-known projection-based clustering algorithm, ORCLUS, and a two-level architecture for high-dimensional data indexing and query is also proposed, which integrates projected clusters and principal axis trees (PAT) to generate efficient high-dimensional data indexes. The query performances of similarity search by ASDS+PAT, ORCLUS+PAT, PAT alone, and Clindex are compared on two high-dimensional data sets. The results show that the integration of ASDS and PAT is an efficient indexing architecture and considerably reduces the query time.
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© 2004 Springer-Verlag Berlin Heidelberg
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Wang, B., Gan, J.Q. (2004). Integration of Projected Clusters and Principal Axis Trees for High-Dimensional Data Indexing and Query. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_28
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DOI: https://doi.org/10.1007/978-3-540-28651-6_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
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