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     Research Journal of Applied Sciences, Engineering and Technology

    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 
Submitted: January 23, 2013
Accepted: February 25, 2013
Published: 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.

Key words:  Clustering, decimal scaling, k-means, min-max, standardization, z-score,
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Cite this Reference:
Ismail Bin Mohamad and Dauda Usman, . Standardization and Its Effects on K-Means Clustering Algorithm. Research Journal of Applied Sciences, Engineering and Technology, (17): 3299-3303.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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