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An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study

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Author(s): M. H. Marghny | Rasha M. Abd El-Aziz | Ahmed I. Taloba

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 34;
Issue: 6;
Start page: 1;
Date: 2011;
Original page

Keywords: Genetic Algorithms | Clustering | K-means algorithm | Squared-error criterion | Hepatitis-C Virus (HCV)

ABSTRACT
Clustering analysis plays an important role in scientific research and commercial application. Kmeans algorithm is a widely used partition method in clustering. However, it is known that the Kmeans algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms 'GAs', reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multisampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.
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