An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study
This work addresses clustering issues for researchers and practitioners dealing with large-scale data, but it appears incremental as it combines existing techniques like genetic algorithms with K-means.
The authors tackled the problem of K-means clustering getting stuck in suboptimal solutions due to poor initial center selection by proposing a method that uses genetic algorithms to select initial centers, applied to public datasets including Hepatitis C. They reported advantages such as reduced sensitivity to isolated points and avoidance of dissevering big clusters, though no concrete numerical results were provided.
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.