2013/03/11 by P. Ashok, Ashok, P., G. M Kadhar Nawaz +6
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Text and Document Classification Technologies #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1304.0725
7 pages, to strengthen the k means algorithm
arxiv created 2013/03/11 · openalex publication_date 2013/03/11 · arxiv updated 2013/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.