A top-down approach for density-based clustering using multidimensional indexes

Cited 3 time in webofscience Cited 0 time in scopus
  • Hit : 291
  • Download : 0
Clustering on large databases has been studied actively as all increasing number of applications involve huge amount of data. In this paper, we propose all efficient top-down approach for density-based clustering, which is based on the density information stored in index nodes of a multidimensional index. We first provide a formal definition of the cluster based on the concept of region contrast partition. Based on this notion, we propose a novel top-down clustering algorithm, which improves the efficiency through branch-and-bound pruning. For this pruning, we present a technique for determining the bounds based oil sparse and dense internal regions and formally prove the correctness of the bounds. Experimental results show that the proposed method reduces the elapsed time by up to 96 times compared with that of BIRCH, which is a well-known clustering method. The results also show that the performance improvement becomes more marked as the size of the database increases. (C) 2003 Elsevier Inc. All rights reserved.
Publisher
ELSEVIER SCIENCE INC
Issue Date
2004-09
Language
English
Article Type
Article
Citation

JOURNAL OF SYSTEMS AND SOFTWARE, v.73, pp.169 - 180

ISSN
0164-1212
URI
http://hdl.handle.net/10203/85192
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0