Selection of variables for cluster analysis and classification rules
نویسندگان
چکیده
In this paper we introduce two procedures for variable selection in cluster analysis and classification rules. One is mainly oriented to detect the “noisy” non–informative variables, while the other deals also with multicolinearity. A forward–backward algorithm is also proposed to make feasible these procedures in large data sets. A small simulation is performed and some real data examples are analyzed.
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تاریخ انتشار 2006