Please use this identifier to cite or link to this item:
|Title:||Efficient mining of distance-based subspace clusters|
|Authors:||Liu, G. |
|Source:||Liu, G.,Sim, K.,Li, J.,Wong, L. (2009). Efficient mining of distance-based subspace clusters. Statistical Analysis and Data Mining 2 (5-6) : 427-444. ScholarBank@NUS Repository. https://doi.org/10.1002/sam.10062|
|Abstract:||Traditional similarity measurements often become meaningless when dimensions of datasets increase. Subspace clustering has been proposed to find clusters embedded in subspaces of high-dimensional datasets. Many existing algorithms use a grid-based approach to partition the data space into nonoverlapping rectangle cells, and then identify connected dense cells as clusters. The rigid boundaries of the grid-based approach may cause a real cluster to be divided into several small clusters. In this paper, we propose to use a sliding-window approach to partition the dimensions to preserve significant clusters. We call this model nCluster model. The sliding-window approach generates more bins than the grid-based approach, thus it incurs higher mining cost. We develop a deterministic algorithm, called MaxnCluster, to mine nClusters efficiently. MaxnCluster uses several techniques to speed up the mining, and it produces only maximal nClusters to reduce result size. Non-maximal nClusters are pruned without the need of storing the discovered nClusters in the memory, which is key to the efficiency of MaxnCluster. Our experiment results show that (i) the nCluster model can indeed preserve clusters that are shattered by the grid-based approach on synthetic datasets; (ii) the nCluster model produces more significant clusters than the grid-based approach on two real gene expression datasets and (iii) MaxnCluster is efficient in mining maximal nClusters. © 2009 Wiley Periodicals, Inc.|
|Source Title:||Statistical Analysis and Data Mining|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 17, 2018
checked on Jan 14, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.