Title: Analyse k-means algorithm and implementing a new clustering algorithm

Authors: H. Parthasarathi Patra; Kommineni Nikitha

Addresses: Department of Computer Science and Engineering, Gayatri Vidyaparishad College of Engineering (A), Madhurawada, AP, India ' Department of Computer Science and Engineering, Gayatri Vidyaparishad College of Engineering (A), Madhurawada, AP, India

Abstract: Clustering is a technique of machine learning which involves grouping data points. We can use clustering algorithm to cluster each data points into a specific group. K-means algorithm is the most famous and commonly used algorithm for analysis of clusters. The k-means clustering algorithm is a method of partitioning clusters that partition data objects into k different clusters. However, the main drawback of this algorithm is that classical k-means algorithm is mainly sensitive to initial centroids and it is also difficult to determine the total number of clusters. So in order to improve the performance of k-means algorithm, this paper presents a new modified k-means method which uses intra-class distance to cluster the data.

Keywords: machine learning; clustering; k-means; centroid; Euclidean distance.

DOI: 10.1504/IJCSYSE.2021.120288

International Journal of Computational Systems Engineering, 2021 Vol.6 No.4, pp.178 - 181

Received: 24 Nov 2020
Accepted: 22 Jul 2021
Published online: 13 Jan 2022 *

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