Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/24896
Title | Initializing k-means clustering algorithm using statistical information |
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Untitled | |
Abstract |
K-means clustering algorithm is one of the best known algorithms used in clustering; nevertheless it has many disadvantages as it may converge to a local optimum, depending on its random initialization of prototypes. We will propose an enhancement to the initialization process of k-means, which depends on using statistical information from the data set to initialize the prototypes. We show that our algorithm gives valid clusters, and that it decreases error and time. |
Authors | |
Type | Journal Article |
Date | 2011 |
Published in | International Journal of Computer Applications |
Series | Volume: 29, Number: 7 |
Publisher | International Journal of Computer Applications, 244 5 th Avenue,# 1526, New York, NY 10001, USA India |
Citation | |
Item link | Item Link |
License | ![]() |
Collections | |
Files in this item | ||
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Ashour, Wesam M._13.pdf | 387.7Kb |