Tree-Based Algorithm for Stable and Efficient Data Clustering

Authors

Hasan Aljabbouli, Colorado State University GlobalFollow
Abdullah Albizri, Montclair State UniversityFollow
Antoine Harfouche, University Paris NanterreFollow

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

https://doi.org/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, Hasan, Abdullah Albizri, and Antoine Harfouche. "Tree-Based Algorithm for Stable and Efficient Data Clustering." In Informatics, vol. 7, no. 4, p. 38. Multidisciplinary Digital Publishing Institute, 2020.