What question did this study set out to answer?

This research aims to develop a more efficient clustering algorithm that reduces computational time while achieving effective clustering results.

January 1, 2014Open Access

Fast Efficient Clustering Algorithm for Balanced Data

Key Points

This research aims to develop a more efficient clustering algorithm that reduces computational time while achieving effective clustering results.
Developed the Fast Balanced K-means (FBK-means) algorithm to improve on traditional k-means
Evaluated the algorithm's performance using balanced data sets
Compared computational time and clustering effectiveness with k-means
FBK-means significantly reduces computational time compared to traditional k-means
Delivers clustering results comparable to k-means on balanced datasets
Indicates strong performance in pattern recognition and data mining tasks

Abstract

The Cluster analysis is a major technique for statistical analysis, machine learning, pattern recognition, data mining, image analysis and bioinformatics. K-means algorithm is one of the most important clustering algorithms. However, the k-means algorithm needs a large amount of computational time for handling large data sets. In this paper, we developed more efficient clustering algorithm to overcome this deficiency named Fast Balanced k-means (FBK-means). This algorithm is not only yields the best clustering results as in the k-means algorithm but also requires less computational time. The algorithm is working well in the case of balanced data.

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Cite This Study

Adel et al. (Wed,) studied this question.

synapsesocial.com/papers/6a1a84227ff99bba0645cd1f https://doi.org/https://doi.org/10.14569/ijacsa.2014.050619

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