Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/24522
Title | DSMK-means “Density-based Split-and-Merge K-means clustering Algorithm” |
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Untitled | |
Abstract |
Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split-and-Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise. |
Authors | |
Type | Journal Article |
Date | 2013 |
Published in | Journal of Artificial Intelligence and Soft Computing Research |
Series | Volume: 3, Number: 1 |
Publisher | De Gruyter Open |
Citation | |
Item link | Item Link |
License | ![]() |
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Files in this item | ||
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Ashour, Wesam M._38.pdf | 2.049Mb |