K+ Means : An Enhancement Over K-Means Clustering Algorithm
Srikanta Kolay, Kumar Sankar Ray, Abhoy Chand Mondal

TL;DR
The paper introduces K+ Means, an improved clustering algorithm that addresses the challenge of determining the optimal number of clusters in K-means, enhancing its effectiveness.
Contribution
It proposes K+ Means, a novel enhancement over K-Means, specifically designed to better handle the selection of the number of clusters.
Findings
K+ Means improves clustering accuracy.
It simplifies the process of choosing K.
Enhanced performance over traditional K-Means.
Abstract
K-means (MacQueen, 1967) [1] is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set to a predefined, say K number of clusters. Determination of K is a difficult job and it is not known that which value of K can partition the objects as per our intuition. To overcome this problem we proposed K+ Means algorithm. This algorithm is an enhancement over K-Means algorithm.
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Taxonomy
TopicsAdvanced Clustering Algorithms Research · Algorithms and Data Compression · Data Management and Algorithms
