FEATURE SUBSET SELECTION FOR HIGH DIMENSIONAL DATA BASED ON CLUSTERING
| Author(s) | : | Prof. S.N.Zaware, Heena Shaikh, Sheefa Shaikh, Asmita Orpe, Pooja Rokade |
| Institution | : | Computer Department, AISSMS IOIT Pune |
| Published In | : | Vol. 2, Issue 12 — December 2015 |
| Page No. | : | 105-107 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Feature selection is the process of evaluating and extracting desired data which can be grouped into subsetswhich retain the integrity of original data. A feature selection algorithm should be efficient and effective. Efficient meansminimum time required and effective means quality of generated subset is not compromised. Our system proposes analgorithm which consists of following steps: Markov Blanket, Shannon Infogain, Minimum Spanning Tree, TreePartition, Gaussian Distribution, Bayesian Probability. Applying these steps we get the desired subset from the clusters.Our system ensures to remove irrelevant data along with redundant data which most of the systems fail to perform.
Prof. S.N.Zaware, Heena Shaikh, Sheefa Shaikh, Asmita Orpe, Pooja Rokade, “FEATURE SUBSET SELECTION FOR HIGH DIMENSIONAL DATA BASED ON CLUSTERING”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 12, pp. 105-107, December 2015.
