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    Abstract
2014 (Vol. 7, Issue: 4)
Article Information:

Outlier Removal Approach as a Continuous Process in Basic K-Means Clustering Algorithm

Dauda Usman and Ismail Bin Mohamad
Corresponding Author:  Dauda Usman 

Key words:  Infectious diseases, k-means clustering, principal component analysis, principal components, standardization, ,
Vol. 7 , (4): 771-777
Submitted Accepted Published
April 04, 2013 April 22, 2013 January 27, 2014
Abstract:

Clustering technique is used to put similar data items in a same group. K-mean clustering is a commonly used approach in clustering technique which is based on initial centroids selected randomly. However, the existing method does not consider the data preprocessing which is an important task before executing the clustering among the different database. This study proposes a new approach of k-mean clustering algorithm. Experimental analysis shows that the proposed method performs well on infectious disease data set when compare with the conventional k-means clustering method.
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  Cite this Reference:
Dauda Usman and Ismail Bin Mohamad, 2014. Outlier Removal Approach as a Continuous Process in Basic K-Means Clustering Algorithm.  Research Journal of Applied Sciences, Engineering and Technology, 7(4): 771-777.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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