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|Title:||Distance based subspace clustering with flexible dimension partitioning|
|Authors:||Liu, G. |
|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|
|Appears in Collections:||Staff Publications|
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