We propose a new class of center-based iterative clustering algorithms, K-Harmonic Means (KHM p ), 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. KHM p 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. KHM p significantly improves the quality of clustering results comparing with both K-Means and EM. The KHM p 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.
K-Harmonic means clustering algorithm addresses the random initial clusters problem by using a different cost function REF for numeric datasets.
Generalized k-harmonic means - dynamic weighting of data in unsupervised learning
First SIAM International Conference on Data Mining
Computer Science