Vector quantization and signal compression
Vector quantization and signal compression
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Categorizing Visitors Dynamically by Fast and Robust Clustering of Access Logs
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Parameter-Free Spatial Data Mining Using MDL
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring the coverage of interest point detectors
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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We propose a new center-based iterative clustering algorithm, KHarmonic Means (KHM), which is essentially insensitive to the initialization of the centers, demonstrated through a set of experiments. The dependency of the K-Means performance on the initialization of the centers has been a major problem; a similar issue exists for an alternative algorithm, Expectation Maximization (EM). Many have tried to generate good initializations to solve the sensitivity problem. KHM 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 significantly improves the quality of clustering results comparing with both K-Means and EM. The KHM algorithm has been implemented in both sequential and parallel languages and tested on hundreds of randomly generated datasets with different data distribution and clustering characteristics.