Scaling EM (Expectation-Maximization) Clustering to Large Databases

Scaling EM (Expectation-Maximization) Clustering to Large Databases (1999)

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by Paul S. Bradley , Usama M. Fayyad , Cory A. Reina , P. S. Bradley , Usama Fayyad , Cory Reina

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Datum	Value	Source
TITLE	Scaling EM (Expectation-Maximization) Clustering to Large Databases	user correction - Legacy Corrections
AUTHOR NAME	Paul S. Bradley	SVM HeaderParse 0.1
AUTHOR AFFIL	Microsoft Research	SVM HeaderParse 0.2
AUTHOR NAME	Usama M. Fayyad	SVM HeaderParse 0.1
AUTHOR NAME	Cory A. Reina	SVM HeaderParse 0.1
AUTHOR AFFIL	Microsoft Research; Microsoft Corporation	SVM HeaderParse 0.2
AUTHOR ADDR	One Microsoft Way; Redmond, WA 98052	SVM HeaderParse 0.1
AUTHOR NAME	P. S. Bradley	SVM HeaderParse 0.1
AUTHOR AFFIL	Abstract; Microsoft Research	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052, USA	SVM HeaderParse 0.1
AUTHOR NAME	Usama Fayyad	SVM HeaderParse 0.1
AUTHOR AFFIL	Abstract; Microsoft Research	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052, USA	SVM HeaderParse 0.1
AUTHOR NAME	Cory Reina	SVM HeaderParse 0.1
AUTHOR AFFIL	Abstract; Microsoft Research	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052, USA	SVM HeaderParse 0.1
ABSTRACT	Practical statistical clustering algorithms typically center upon an iterative refinement optimization procedure to compute a locally optimal clustering solution that maximizes the fit to data. These algorithms typically require many database scans to converge, and within each scan they require the access to every record in the data table. For large databases, the scans become prohibitively expensive. We present a scalable implementation of the Expectation-Maximization (EM) algorithm. The database community has focused on distance-based clustering schemes and methods have been developed to cluster either numerical or categorical data. Unlike distancebased algorithms (such as K-Means), EM constructs proper statistical models of the underlying data source and naturally generalizes to cluster databases containing both discrete-valued and continuous-valued data. The scalable method is based on a decomposition of the basic statistics the algorithm needs: identifying regions of the data that...	user correction - Legacy Corrections
YEAR	1999	user correction - Legacy Corrections
CITATIONS	39 found	ParsCit 1.0