DocumentCode :
3532205
Title :
DiSCl: Distributed Intelligent Subspace Clustering, a density based clustering approach for very high dimensional distributed dataset
Author :
Jahirabadkar, Sunita ; Kulkarni, Parag
Author_Institution :
Cummins Coll. of Eng., Pune, India
fYear :
2009
fDate :
28-31 July 2009
Firstpage :
550
Lastpage :
551
Abstract :
In this paper, a problem called, ldquoDistributed Subspace Clustering for high dimensional distributed database, based on density notion of clusteringrdquo is explored. To solve this problem, we described our algorithm ISC (Intelligent Subspace Clustering) which uses the concept called Hierarchical Subspace Clustering. ISC finds the input parameter epsi i.e. the distance, required for any density based clustering, adaptively at various levels of dimensionalities. This gives the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.
Keywords :
deductive databases; distributed databases; learning (artificial intelligence); pattern clustering; distributed intelligent subspace clustering; hierarchical subspace clustering; high dimensional distributed database; incremental learning; Clustering algorithms; Couplings; Deductive databases; Distributed databases; Educational institutions; Testing; Cluster Density; Distributed Clustering; High dimensional data; Subspace Clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Networked Digital Technologies, 2009. NDT '09. First International Conference on
Conference_Location :
Ostrava
Print_ISBN :
978-1-4244-4614-8
Electronic_ISBN :
978-1-4244-4615-5
Type :
conf
DOI :
10.1109/NDT.2009.5272086
Filename :
5272086
Link To Document :
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