Volume 6, Issue 1, August 2014, Pages 75–81
K. Revathi1 and T. Kalai Selvi2
1 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
2 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Process with high dimensional data is enormous issue in data mining and machine learning applications. Feature selection is the mode of recognize the good number of features that produce well-suited outcome as the unique entire set of features. Feature selection process constructs a pathway to reduce the dimensionality and time complexity and also improve the accuracy level of classifier. In this paper, we use an alternative approach, called affinity propagation algorithm for effective and efficient feature selection and clustering process. The endeavor is to improve the performance in terms accuracy and time complexity.
Author Keywords: Classification, Data mining, Feature selection, Feature clustering.
K. Revathi1 and T. Kalai Selvi2
1 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
2 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Process with high dimensional data is enormous issue in data mining and machine learning applications. Feature selection is the mode of recognize the good number of features that produce well-suited outcome as the unique entire set of features. Feature selection process constructs a pathway to reduce the dimensionality and time complexity and also improve the accuracy level of classifier. In this paper, we use an alternative approach, called affinity propagation algorithm for effective and efficient feature selection and clustering process. The endeavor is to improve the performance in terms accuracy and time complexity.
Author Keywords: Classification, Data mining, Feature selection, Feature clustering.
How to Cite this Article
K. Revathi and T. Kalai Selvi, “EFFICIENT AND EFFECTIVE SUBSET SELECTION PROCESS BASED ON CLUSTERING ALGORITHM,” International Journal of Innovation and Scientific Research, vol. 6, no. 1, pp. 75–81, August 2014.
