Year: 2013
Type: article
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.
Authors: Dauda Usman, Ismail Bin Mohamad
Institution: University of Technology Malaysia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2184638288