Tree-based algorithm for stable and efficient data clustering

Hasan Aljabbouli, Abdullah Albizri, Antoine Harfouche

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article number7040038
Issue number4
StatePublished - Sep 2020


  • Data clustering
  • K-means algorithm
  • Kd-tree structure


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