An Efficient Hybrid Algorithm for Data Clustering Using Improved Genetic Algorithm and Nelder Mead Simplex Search

Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper presents data clustering using improved genetic algorithm (IGA) and the popular Nelder-Me...

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Bibliographic Details
Main Authors: Satapathy, S.C., Murthy, J.V.R., Prasada Reddy, P.V.G.D.
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Clustering algorithms
Computational intelligence
Convergence
Educational institutions
Genetic algorithms
Genetic mutations
Iterative algorithms
Optimization methods
Performance evaluation
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Summary:Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper presents data clustering using improved genetic algorithm (IGA) and the popular Nelder-Mead(NM) Simplex search . To improve the accuracy of data clustering, an improved GA (IGA) is used. The performance of IGA is established with many benchmark test functions optimization. To accelerate the clustering process further more a hybrid algorithm based on improved GA and Nelder-Mead simplex search(NM) is suggested for clustering and is tested on 7 datasets and its performance is compared with above two algorithms and the traditional K-means algorithm.
DOI:10.1109/ICCIMA.2007.183