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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:
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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.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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