On Initial Effects of the k-Means Clustering
There are many research studies conducted in order to find a more optimal way to initialize the k-means algorithm, also referred to as Lloyd's algorithm. Despite the appreciated efficiency of the k-means process, occasionally it may return a less than optimal clustering solution. It is widely b...
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Published in | Proceedings of the International Conference on Scientific Computing (CSC) p. 200 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
Athens
The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
01.01.2015
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Online Access | Get full text |
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Summary: | There are many research studies conducted in order to find a more optimal way to initialize the k-means algorithm, also referred to as Lloyd's algorithm. Despite the appreciated efficiency of the k-means process, occasionally it may return a less than optimal clustering solution. It is widely believed that modifications to the initialization process will improve results. Here, the choice of initial centroids for the k-means clustering technique is reviewed with respect to efficiency in stabilizing or convergence with different initialization methods. Several proposed initialization techniques are evaluated on a two dimensional model in an attempt to verify or reproduce results similar to those of the studies chosen. |
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