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Normalization based K means Clustering Algorithm

Machine Learning 2015-03-04 v1 Databases

Abstract

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing K-means clustering algorithm in terms of complexity and overall performance.

Keywords

Cite

@article{arxiv.1503.00900,
  title  = {Normalization based K means Clustering Algorithm},
  author = {Deepali Virmani and Shweta Taneja and Geetika Malhotra},
  journal= {arXiv preprint arXiv:1503.00900},
  year   = {2015}
}

Comments

5 pages, 4 figures in International Journal of Advanced Engineering Research and Science (IJAERS)-Feb 2015

R2 v1 2026-06-22T08:42:59.486Z