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.