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

Content Provider	Scilit
Author	Ramkumar, P. Kalamani, P. Valarmathi, C. Devi, M. Sheela
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series 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.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1979/1/012015/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1979/1/012015
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1979
Language	English
Publisher	IOP Publishing
Publisher Date	2021-08-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Distance Based K Means Clustering Algorithm
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy