A Shrinking-Based Approach for Multi-Dimensional Data Analysis

Summary: Shrinking, inspired by Newton's law of gravitation, reorganizes data by moving points along density gradients. The approach uses three steps: shrinking, grid clustering across cell sizes, and multi-scale evaluation to pick clusters. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 8995
Venue: VLDB
Year: 2003
Pagerank: 4.1945683e-05
Overall Rank: 12,622 | 12.20%
DOI: -

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
27	Efficient and Effective Clustering Methods for Spatial Data Mining	1994	VLDB	0.00080736878
33	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00077324389
270	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00029505642
277	Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications	1998	SIGMOD	0.00029311426
341	CURE: An Efficient Clustering Algorithm for Large Databases	1998	SIGMOD	0.00026810548
802	Optimal Multi-Step k-Nearest Neighbor Search	1998	SIGMOD	0.00016502317
1,097	STING : A Statistical Information Grid Approach to Spatial Data Mining	1997	VLDB	0.00014119975
1,538	WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases	1998	VLDB	0.00011464884
1,595	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00011222442

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