15-02-2013, 09:53 AM
CLUSTERING USING FUZZY ROUGH SET FEATURE SELECTION
CLUSTERING.ppt (Size: 683.5 KB / Downloads: 55)
ABSTRACT
Clustering also a form of data grouping, groups a set of data such that intra cluster similarity is maximized and the inter cluster similarity is minimized.
Using feature selection method unimportant or irrelevant features are eliminated, and smaller set of attributes are generated.
FRFS(Fuzzy Rough Feature Selection) and antFRFS is used for feature selection.
Selected features are clustered using SDR(Standard Deviation Roughness) algorithm.
Performance is compared.
Rough set theory
Rough set theory can be regarded as a new mathematical tool for imperfect data analysis.
The theory has found applications in many domains, such as decision support, engineering, environment, banking, medicine and others.
OBJECTIVE
The main objective is to compare the accuracy of two feature selection algorithm namely FRFS(Fuzzy Rough Feature Selection) and antFRFS algorithm in terms of clustering.
PROBLEM STATEMENT
Selecting features using two algorithms, FRFS and antFRFS separately and comparing their accuracy in clustering using SDR clustering.
CONCLUSION
The study states clustering using Fuzzy Rough Feature Selection or antFRFS will give best accuracy, depending on the future work.