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    Abstract
2013 (Vol. 6, Issue: 17)
Article Information:

Standardization and Its Effects on K-Means Clustering Algorithm

Ismail Bin Mohamad and Dauda Usman
Corresponding Author:  Dauda Usman 

Key words:  Clustering, decimal scaling, k-means, min-max, standardization, z-score,
Vol. 6 , (17): 3299-3303
Submitted Accepted Published
January 23, 2013 February 25, 2013 September 20, 2013
Abstract:

Data clustering is an important data exploration technique with many applications in data mining. K- means is one of the most well known methods of data mining that partitions a dataset into groups of patterns, many methods have been proposed to improve the performance of the K-means algorithm. Standardization is the central preprocessing step in data mining, to standardize values of features or attributes from different dynamic range into a specific range. In this paper, we have analyzed the performances of the three standardization methods on conventional K-means algorithm. By comparing the results on infectious diseases datasets, it was found that the result obtained by the z-score standardization method is more effective and efficient than min-max and decimal scaling standardization methods.
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  Cite this Reference:
Ismail Bin Mohamad and Dauda Usman, 2013. Standardization and Its Effects on K-Means Clustering Algorithm.  Research Journal of Applied Sciences, Engineering and Technology, 6(17): 3299-3303.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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