Document Type
Article
Publication Date
9-27-2020
Journal / Book Title
Informatics
Abstract
The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm using a K-dimensional tree (Kd-tree) data structure. The proposed Kd-tree is utilized as a data structure to enhance the choice of initial centers of the clusters and to reduce the number of the nearest neighbor searches required by the algorithm. The developed framework also includes an efficient center insertion technique leading to an incremental operation that overcomes the instability problem of the K-means algorithm. The results of the proposed algorithm were compared with those obtained from the K-means algorithm, K-medoids, and K-means++ in an experiment using six different datasets. The results demonstrated that the proposed algorithm provides superior and more stable clustering solutions.
DOI
10.3390/informatics7040038
MSU Digital Commons Citation
Aljabbouli, Hasan; Albizri, Abdullah; and Harfouche, Antoine, "Tree-Based Algorithm for Stable and Efficient Data Clustering" (2020). Department of Information Management and Business Analytics Faculty Scholarship and Creative Works. 154.
https://digitalcommons.montclair.edu/infomgmt-busanalytics-facpubs/154
Published Citation
Aljabbouli, H., Albizri, A., & Harfouche, A. (2020). Tree-Based Algorithm for Stable and Efficient Data Clustering. Informatics, 7(4), 38. https://doi.org/10.3390/informatics7040038
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).