International Journal of Application or Innovation in Engineering & Management
An Inspiration for Recent Innovation & Research….
ISSN 2319 – 4847
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Title: |
Improvised Method Of FAST Clustering Based Feature Selection Technique Algorithm For High Dimensional Data
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Author Name: |
Mr.Avinash Godase,Ms.Poonam Gupta |
Abstract: |
ABSTRACT
A high dimensional data is data consisting thousands of attributes or features. Nowadays for scientific and research
applications high dimensional data is used.But.as there are thousands of features present in the data, We need to select the
features those are non-redundant and most relevant in order to reduce the dimensionality and runtime, and also improve
accuracy of the results. In this paper we have proposed FAST algorithm of feature subset selection and improved method of
FAST algorithm. The efficiency and accuracy of the results is evaluated by empirical study. In this paper, we have presented a
novel clustering-based feature subset selection algorithm for high dimensional data. The algorithm involves (i) removing
irrelevant features, (ii) constructing a minimum spanning tree from relative ones, and (iii) partitioning the MST and selecting
representative features. In the proposed algorithm, a cluster consists of features. Each cluster is treated as a single feature and
thus dimensionality is highly reduced. The Proposed System will be Implementation of FAST algorithm Using Dice Coefficient
Measure to remove irrelevant and redundant features.
Keywords: FAST, Feature Subset Selection, Graph Based Clustering, Minimum Spanning Tree. |
Cite this article: |
Mr.Avinash Godase,Ms.Poonam Gupta , "
Improvised Method Of FAST Clustering Based Feature Selection Technique Algorithm For High Dimensional Data " , International Journal of Application or Innovation in Engineering & Management (IJAIEM),
Volume 4, Issue 6, June 2015 , pp.
135-140 , ISSN 2319 - 4847.
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