Automatically assigned DDC number: 006312
Manually assigned DDC number: 006312

Number of references: 32

Title: Simultaneous Feature Selection and Clustering Using Mixture Models

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Subject: Martin H. C. Law,Mario A. T. Figueiredo,Anil K. Jain Simultaneous Feature Selection and Clustering Using Mixture Models

Description: Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is di#cult because, unlike in supervised learning, there are no class labels for the data and so no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue.

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2004-03-05

Pubyear: 2004

Format: pdf

Identifier: http://citeseer.ist.psu.edu/676182.html

Source: http://www.lx.it.pt/%7Emtf/IEEE_TPAMI_2004_Law_Figueiredo_Jain.pdf

Language: en

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