Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{144530,
        author = {Geeta Rani and Sachin Shrivastava},
        title = {An optimized k-means algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {12},
        pages = {126-129},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144530},
        abstract = {Data mining is a new technology, developing with database and artificial intelligence. It is a processing procedure of extracting credible, novel, effective and understandable patterns from database. Cluster analysis is an important data mining technique used to find data segmentation and pattern information. By clustering the data, people can obtain the data distribution, observe the character of each cluster, and make further study on particular clusters. Cluster analysis method is one of the most analytical methods of data mining. The method will directly influence the result of clustering. This paper discusses the standard of k-mean clustering and analyzes the shortcomings of standard k-means such as k-means algorithm calculates distance of each data point from each cluster centre. Calculating this distance in each iteration makes the algorithm of low efficiency. This paper introduces an optimized algorithm which solves this problem. This is done by introducing a simple data structure to store some information in every iteration and used this information in next iteration.
},
        keywords = {Data mining ,k-means algorithm,Clustering},
        month = {},
        }