Title :
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
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;
Conference_Titel :
Computer Communication and Informatics (ICCCI), 2014 International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4799-2353-3
DOI :
10.1109/ICCCI.2014.6921721
