Paper Info
Title
SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters
Abstract
Clustering is one of the fundamental data mining tasks. Many different clustering paradigms have been developed over the years, which include partitional, hierarchical, mixture model based, density-based, spectral, subspace, and so on. The focus of this paper is on full-dimensional, arbitrary shaped clusters. Existing methods for this problem suffer either in terms of the memory or time complexity (quadratic or even cubic). This shortcoming has restricted these algorithms to datasets of moderate sizes. In this paper we propose SPARCL, a simple and scalable algorithm for finding clusters with arbitrary shapes and sizes, and it has linear space and time complexity. SPARCL consists of two stages—the first stage runs a carefully initialized version of the Kmeans algorithm to generate many small seed clusters. The second stage iteratively merges the generated clusters to obtain the final shape-based clusters. Experiments were conducted on a variety of datasets to highlight the effectiveness, efficiency, and scalability of our approach. On the large datasets SPARCL is an order of magnitude faster than the best existing approaches.
Year
DOI
Venue
2009
10.1007/s10115-009-0216-0
Knowl. Inf. Syst.
Keywords
DocType
Volume
arbitrary shape-based cluster,stage iteratively,final shape-based cluster,kmeans algorithm,efficient algorithm,time complexity,arbitrary shape,clustering · spatial · kmeans · hierarchical · linear time,large datasets sparcl,arbitrary shaped cluster,existing approach,scalable algorithm,different clustering paradigm,linear space,kmeans,data mining,hierarchical,mixture model,spatial,linear time,clustering
Journal
21
Issue
ISSN
Citations 
2
0219-3116
14
PageRank 
References 
Authors
0.70
31
4
Name
Order
Citations
PageRank
Vineet Chaoji142819.50
Mohammad Al Hasan242735.08
Saeed Salem318217.39
Mohammed Javeed Zaki47972536.24