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Abstract
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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
Submitted: April 04, 2013
Accepted: April 22, 2013
Published: January 27, 2014 |
Abstract:
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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.
Key words: Infectious diseases, k-means clustering, principal component analysis, principal components, standardization, ,
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Abstract
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Cite this Reference:
Dauda Usman and Ismail Bin Mohamad, . Outlier Removal Approach as a Continuous Process in Basic K-Means Clustering Algorithm. Research Journal of Applied Sciences, Engineering and Technology, (4): 771-777.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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