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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.

A Fast Deterministic Kmeans Initialization

Omar Kettani, Faical Ramdani. Published in Information Sciences

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/ijais2017451683
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  1. Omar Kettani and Faical Ramdani. A Fast Deterministic Kmeans Initialization. International Journal of Applied Information Systems 12(2):6-11, May 2017. URL, DOI BibTeX

    @article{10.5120/ijais2017451683,
    	author = "Omar Kettani and Faical Ramdani",
    	title = "A Fast Deterministic Kmeans Initialization",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "May 2017",
    	volume = 12,
    	number = 2,
    	month = "May",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "6-11",
    	url = "http://www.ijais.org/archives/volume12/number2/984-2017451683",
    	doi = "10.5120/ijais2017451683",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

The k-means algorithm remains one of the most widely used clustering methods, in spite of its sensitivity to the initial settings. This paper explores a simple, computationally low, deterministic method which provides k-means with initial seeds to cluster a given data set. It is simply based on computing the means of k samples with equal parts taken from the given data set. We test and compare this method to the related well know kkz initialization algorithm for k-means, using both simulated and real data, and find it to be more efficient in many cases.

Reference

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Keywords

k-means initialization, kkz