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Reseach Article

An Improved Agglomerative Clustering Method

by Omar Kettani, Faical Ramdani
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

@article{ 10.5120/ijais2017451689,
author = { Omar Kettani, Faical Ramdani },
title = { An Improved Agglomerative Clustering Method },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2017 },
volume = { 12 },
number = { 3 },
month = { June },
year = { 2017 },
issn = { 2249-0868 },
pages = { 16-23 },
numpages = {9},
url = { },
doi = { 10.5120/ijais2017451689 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-07-05T19:07:58.339252+05:30
%A Omar Kettani
%A Faical Ramdani
%T An Improved Agglomerative Clustering Method
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 3
%P 16-23
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

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.

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Index Terms

Computer Science
Information Sciences


Clustering k-means k-means++ KKZ