|International Journal of Applied Information Systems
|Foundation of Computer Science (FCS), NY, USA
|Volume 12 - Number 3
|Year of Publication: 2017
|Authors: Omar Kettani, Faical Ramdani
Omar Kettani, Faical Ramdani . An Improved Agglomerative Clustering Method. International Journal of Applied Information Systems. 12, 3 ( June 2017), 16-23. DOI=10.5120/ijais2017451689
Clustering is a common and useful exploratory task widely used in Data mining. Among the many existing clustering algorithms, the Agglomerative Clustering Method (ACM) introduced by the authors suffers from an obvious drawback: its sensitivity to data ordering. To overcome this issue, we propose in this paper to initialize the ACM by using the KKZ seed algorithm. The proposed approach (called KKZ_ACM) has a lower computational time complexity than the famous k-means algorithm. We evaluated its performance by applying on various benchmark datasets and compare with ACM, k-means++ and KKZ_ k-means. Our performance studies have demonstrated that the proposed approach is effective in producing consistent clustering results in term of average Silhouette index.