Finding the Optimal Clusters from Discrete Data using Evolutionary Clustering Algorithm

To find the natural number of clusters in a dataset has been a difficult issue in many clustering algorithms. It is complex to determine the desired number of clusters. Even if a hierarchical tree of clusters is given, it is still hard to determine the cut points. The proposed algorithm is modified...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced research in computer science 2014-04, Vol.5 (4)
Main Authors: Chaudhari, P M, Dharaskar, R V, Thakare, V M
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Computer science
Data mining
Data warehouses
Datasets
Euclidean space
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To find the natural number of clusters in a dataset has been a difficult issue in many clustering algorithms. It is complex to determine the desired number of clusters. Even if a hierarchical tree of clusters is given, it is still hard to determine the cut points. The proposed algorithm is modified to find the optimal number of clusters. It is varied in several ways. It allows a variable number of clusters in the chromosomes. It introduces split and merge mutation and new crossover operations. In addition, the fitness function is enhanced to give a fair evaluation. The experimental results illustrate the proposed algorithm can successfully find the correct number of clusters in many datasets.
ISSN:0976-5697