Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDE.2007.368985
Title: Distance based subspace clustering with flexible dimension partitioning
Authors: Liu, G. 
Li, J.
Sim, K.
Wong, L. 
Issue Date: 2007
Source: Liu, G.,Li, J.,Sim, K.,Wong, L. (2007). Distance based subspace clustering with flexible dimension partitioning. Proceedings - International Conference on Data Engineering : 1250-1254. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2007.368985
Abstract: Traditional similarity or distance measurements usually become meaningless when the dimensions of the datasets increase, which has detrimental effects on clustering performance. In this paper, we propose a distance-based subspace clustering model, called nCiuster, to find groups of objects that have similar values on subsets of dimensions. Instead of using a grid based approach to partition the data space into non-overlapping rectangle cells as in the density based subspace clustering algorithms, the nCiuster model uses a more flexible method to partition the dimensions to preserve meaningful and significant clusters. We develop an efficient algorithm to mine only maximal nClusters. A set of experiments are conducted to show the efficiency of the proposed algorithm and the effectiveness of the new model in preserving significant clusters. © 2007 IEEE.
Source Title: Proceedings - International Conference on Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/41564
ISBN: 1424408032
ISSN: 10844627
DOI: 10.1109/ICDE.2007.368985
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