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January Edition 2018

International Journal of Applied Information Systems solicits high quality original research papers for the January 2018 Edition of the journal. The last date of research paper submission is December 15, 2017.

An Improved Agglomerative Clustering Method

Omar Kettani, Faical Ramdani. Published in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Omar Kettani, Faical Ramdani
10.5120/ijais2017451689
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  1. Omar Kettani and Faical Ramdani. An Improved Agglomerative Clustering Method. International Journal of Applied Information Systems 12(3):16-23, June 2017. URL, DOI BibTeX

    @article{10.5120/ijais2017451689,
    	author = "Omar Kettani and 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",
    	url = "http://www.ijais.org/archives/volume12/number3/988-2017451689",
    	doi = "10.5120/ijais2017451689",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

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.

Reference

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Keywords

Clustering, k-means, k-means++, KKZ