Efficiently exploring clusters using genetic algorithm and fuzzy rules

Author

Pitambare, Dinesh P. ; Kamde, Pravin M.

Author_Institution

Comput. (Networks), Sinhgad Coll. of Eng., Pune, India

fYear

2014

fDate

3-5 Jan. 2014

Firstpage

1

Lastpage

6

Abstract

Cluster is bunch of similar items. Unsupervised classification of patterns into clusters is known as clustering. It is useful in knowledge discovery in data. Clustering is able to deal with different data types. Fuzzy rules are used for data intelligence illustration purpose. User gets highly interpretable discovered clusters using fuzzy rules. To generate accurate fuzzy rules triangular membership function is used. This paper is proposed to automatically explore the number of clusters efficiently from a given numeric dataset. To discover clusters efficiently genetic algorithm is used. Fuzzy rules are generated from genetic algorithm, whose derivative is best fuzzy rules. Best rules are obtained among generated fuzzy rules according to maximum fitness value. Proposed work is carried out on benchmark numeric datasets to validate the capability of the proposed system.

Keywords

data mining; fuzzy set theory; genetic algorithms; pattern classification; pattern clustering; unsupervised learning; data intelligence; data types; fuzzy rule triangular membership function; genetic algorithm; interpretable discovered clusters; knowledge discovery; unsupervised pattern classification; Accuracy; Bellows; Classification algorithms; Clustering algorithms; Computers; Genetic algorithms; Iris; Clustering; best rule; fuzzy clustering; fuzzy rule; fuzzy set theory; genetic algorithm; triangular membership function;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communication and Informatics (ICCCI), 2014 International Conference on

Conference_Location

Coimbatore

Print_ISBN

978-1-4799-2353-3

Type

conf

DOI

10.1109/ICCCI.2014.6921721

Filename

6921721