The full text of this article
Enhancing clustering accuracy by finding initial centroid using k-minimum-average-maximum method
by S. Dhanabal; S. Chandramathi
International Journal of Information and Communication Technology (IJICT), Vol. 11, No. 2, 2017
Abstract: Determining 'the initial seed for clustering' is an issue in k-means which has attracted considerable interest, especially in recent years. Despite its popularity among clustering algorithms, k-means still has many problems such as converging to the local optimum solutions, the results obtained are strongly depends upon the selection of initial seeds, number of clusters need to be known in advance etc. Various initialisation methods were proposed to improve the performance of k-means algorithm. In this paper, a novel approach, k-minimum-average-maximum (k-MAM), is proposed for finding the initial centroids by considering distance on extreme ends. The proposed algorithm is tested with UCI repository datasets and data collected from Facebook. We compared our proposed method with simple k-means and k-means++ initialisation method based on efficiency and effectiveness. The results show that the proposed algorithm converges very fast with better accuracy.
Online publication date: Mon, 04-Sep-2017
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable).
See our Orders page to subscribe.
If you still need assistance, please email email@example.com