An Effective Analysis of Data Clustering using Distance-based K- Means Algorithm

Abstract

Abstract Real-world data sets are regularly provides different and complementary features of information in an unsupervised way. Different types of algorithms have been proposed recently in the genre of cluster analysis. It is arduous to the user to determine well in advance which algorithm would be the most suitable for a given dataset. Techniques with respect to graphs are provides excellent results for this task. However, the existing techniques are easily vulnerable to outliers and noises with limited idea about edges comprised in the tree to divide a dataset. Thus, in some fields, the necessacity for better clustering algorithms it uses robust and dynamic methods to improve and simplify the entire process of data clustering has become an important research field. In this paper, a novel distance-based clustering algorithm called the entropic distance based K-means clustering algorithm (EDBK) is proposed to eradicate the outliers in effective way. This algorithm depends on the entropic distance between attributes of data points and some basic mathematical statistics operations. In this work, experiments are carry out using UCI datasets showed that EDBK method which outperforms the existing methods such as Artificial Bee Colony (ABC), k-means.