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Generalized k-harmonic means - dynamic weighting of data in unsupervised learning

By Bin Zhang


Abstract We propose a new class of center-based iterative clustering algorithms, K-Harmonic Means (KHMp), which is essentially insensitive to the initialization of the centers, demonstrated through many experiments. The insensitivity to initialization is attributed to a dynamic weighting function, which increases the importance of the data points that are far from any centers in the next iteration. The dependency of the K-Means ’ and EM’s performance on the initialization of the centers has been a major problem. Many have tried to generate good initializations to solve the sensitivity problem. KHMp addresses the intrinsic problem by replacing the minimum distance from a data point to the centers, used in K-Means, by the Harmonic Averages of the distances from the data point to all centers. KHMp significantly improves the quality of clustering results comparing with both K-Means and EM. The KHMp algorithms have been implemented in both sequential and parallel languages and tested on hundreds of randomly generated datasets with different data distribution and clustering characteristics

Topics: K-Means, K-Harmonic Means, EM, Data Mining, Dynamic Modeling
Year: 2001
DOI identifier: 10.1137/1.9781611972719.6
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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