Most Read Research Articles Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Adaptivity and Adaptability of Learning Object’s Interface Enhanced TCP Westwood Congestion Avoidance Mechanism (TCP WestwoodNew) Migration of Legacy Information System based on Business Process Theory HomeArchivesVolume 34Number 6An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Call for Paper - July 2015 Edition IJCA solicits original research papers for the July 2015 Edition. Last date of manuscript submission is June 20, 2015. Read More An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study E-mail Print inShare Download Full Text International Journal of Computer Applications © 2011 by IJCA Journal Volume 34 - Number 6 Year of Publication: 2011 Authors: M. H. Marghny Rasha M. Abd El-Aziz Ahmed I. Taloba 10.5120/4092-5420 M H Marghny, Rasha Abd M El-Aziz and Ahmed I Taloba. Article: An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study. International Journal of Computer Applications 34(6):1-6, November 2011. Full text available. BibTeX Abstract Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means 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 multi-sampling. 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.