Clustering by hybrid K-Means and black hole entropic fuzzy clustering algorithm for medical data

Abstract

Today clustering-based machine learning algorithms are the important field in data mining. Here, medical data clustering is one of the core applications of data mining to predict and identify the risk factor of the disease. At the same time, medical data clustering is a very important and challenging task due to its complexity and high frequency of data. In order to achieve proper data clustering, this paper proposed a hybrid data clustering algorithm by the combination of [Formula: see text]-Means and Black Hole Entropic Fuzzy Clustering (BHEFC). [Formula: see text]-Means is the first and one of the most popular and low-computation cost partitioned-based clustering algorithms. There are two modules in this hybrid clustering, first some number of iterations are executed by the first module of this hybrid clustering algorithm, which is [Formula: see text]-Means clustering. After some number of iterations, the clustering solutions are shifted to the second module of this hybrid clustering algorithm, which is Entrophic Fuzzy Clustering. So, it can get the advantages of both algorithms. [Formula: see text]-Means clustering algorithm can produce fast clustering solution due to its low-computation cost. But it can go for premature convergence. To overcome this problem, the second module used BHEFC, which can use large amount of high-frequency medical data. The experimental results are done with the medical practitioners to predict the risk factors of the heart disease patients and doctors can give the suggestions based on the risk factors. Finally, the efficiency of the proposed Hybrid [Formula: see text]-Means and BHEFC is analyzed by three different performance measures.