  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.
