Normalization based K means Clustering Algorithm

TL;DR

This paper introduces a normalization-based K-means clustering algorithm that improves performance and reduces complexity by normalizing data beforehand and calculating initial centroids using weights.

Contribution

The paper proposes a novel normalization-based K-means algorithm with weighted initial centroid calculation, enhancing clustering efficiency and effectiveness.

Findings

N-K means outperforms standard K-means in accuracy

Reduced computational complexity with N-K means

Improved clustering stability and convergence

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.