tailieunhanh - Feature selection and replacement by clustering attributes

Feature selection is to find useful and relevant features from an original feature space to effectively represent and index a given dataset. It is very important for classification and clustering problems, which may be quite difficult to solve when the amount of attributes in a given training data is very large. | Vietnam J Comput Sci 2014 1 47-55 DOI s4O595-013-0004-3 REGULAR PAPER Feature selection and replacement by clustering attributes Tzung-Pei Hong Yan-Liang Liou Shyue-Liang Wang Bay Vo Received 2 October 2013 Accepted 3 October 2013 Published online 9 November 2013 The Author s 2013 Abstract Feature selection is to find useful and relevant features from an original feature space to effectively represent and index a given dataset. It is very important for classification and clustering problems which may be quite difficult to solve when the amount of attributes in a given training data is very large. They usually need a very time-consuming search to get the features desired. In this paper we will try to select features based on attribute clustering. A distance measure for a pair of attributes based on the relative dependency is proposed. An attribute clustering algorithm called Most Neighbors First is also presented to cluster the attributes into a fixed number of groups. The representative attributes found in the clusters can be used for classification such that the whole feature space can be greatly reduced. Besides if the This is an extended version of the paper presented in The Sixth International Conference on Machine Learning and Cybernetics. . Hong Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung Taiwan . Liou Department of Electrical Engineering National University of Kaohsiung Kaohsiung Taiwan e-mail egghead221@ S. -L. Wang Department of Information Management National University of Kaohsiung Kaohsiung Taiwan e-mail slwang@ T. -P. Hong B Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan e-mail tphong@ B. Vo Ton Duc Thang University Ho Chi Minh Vietnam e-mail vdbay@ values of some representative attributes cannot be obtained from current environments for inference some other possible attributes in the same .

• Quản trị mạng
• Attribute clustering
• Feature selection
• Representative attribute
• Relative dependency
• Attribute clustering algorithm
• Data mining
• Hierarchical clustering
• Categorical data
• Rough sets
• Clustering attribute selection