A Framework for Feature Selection in Clustering
نویسندگان
چکیده
منابع مشابه
A New Framework for Distributed Multivariate Feature Selection
Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...
متن کاملA framework for feature selection in clustering.
We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observat...
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Clustering is an important data mining task Data mining often concerns large and high dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high dimensionality or both Di erent features a ect clusters di erently some are important for clusters while others may hinder the clustering task An e cient way of handling it is by selecting ...
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In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for ...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2010
ISSN: 0162-1459,1537-274X
DOI: 10.1198/jasa.2010.tm09415