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ICPR
2010
IEEE

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On Dynamic Weighting of Data in Clustering with K-Alpha Means

13 years 3 months ago

On Dynamic Weighting of Data in Clustering with K-Alpha Means

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Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances.

Sibao Chen, Haixian Wang, Bin Luo

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Clustering Performances | Computer Vision | ICPR 2010 | Initial Centers | K-means |

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Added	13 Feb 2011
Updated	13 Feb 2011
Type	Journal
Year	2010
Where	ICPR
Authors	Sibao Chen, Haixian Wang, Bin Luo

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