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A Clustering - Based Attribute Selection Approach for High Dimensional Data

Ravi P. Patki*

Assistant Professor, Department of Information Technology, International Institute of Information Technology, Pune, India.

Periodicity:June - August'2017
DOI : https://doi.org/10.26634/jit.6.3.13775

Abstract

Attribute selection is the procedure of selecting a subset of important attributes for utilization in model development. The central supposition when utilizing as attribute selection method is that the information contains numerous redundant or irrelevant attributes. Repetitive attributes are those which give no more data than the right now chosen attributes, and irrelevant attributes give no helpful data in any setting. Attribute selection is a process in which subset of important attribute is selected which produce good results. Attribute selection algorithm is used for that purpose which achieve efficiency, i.e. less time and correctness of subset. Existing system proposed clustering based attribute selection algorithm based on efficiency and effectiveness criteria. In this algorithm, attributes are first separated into different clusters using graph theoretic clustering method and then those attributes are selected from each clusters, which is most related to target class. Because of large attributes minimal in graph many nodes are generated and in such situations working of prims algorithm is better. In this paper, the system uses Kruskals algorithm instead of Prims algorithm for better efficiency and accuracy. The Kruskals algorithm perform sorting according to the weight and starts from the smallest one which will take less time to iterate. This is the only method which uses sorting technique which will increase the efficiency.

Keywords

Attribute Subset Selection, Attribute Clustering, Data Mining, Filter Method, Graph-Based Clustering.

How to Cite this Article?

Patki, R. P. (2017). A Clustering - Based Attribute Selection Approach for High Dimensional Data. i-manager’s Journal on Information Technology, 6(3), 8-14. https://doi.org/10.26634/jit.6.3.13775

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