## A Fast and Robust General Purpose Clustering Algorithm (2000)

Venue: | In Pacific Rim International Conference on Artificial Intelligence |

Citations: | 16 - 2 self |

### BibTeX

@INPROCEEDINGS{Estivill-Castro00afast,

author = {Vladimir Estivill-Castro and Jianhua Yang},

title = {A Fast and Robust General Purpose Clustering Algorithm},

booktitle = {In Pacific Rim International Conference on Artificial Intelligence},

year = {2000},

pages = {208--218},

publisher = {Springer}

}

### Years of Citing Articles

### OpenURL

### Abstract

General purpose and highly applicable clustering methods are usually required during the early stages of knowledge discovery exercises. k-Means has been adopted as the prototype of iterative model-based clustering because of its speed, simplicity and capability to work within the format of very large databases. However, k-Means has several disadvantages derived from its statistical simplicity. We propose an algorithm that remains very efficient, generally applicable, multi-dimensional but is more robust to noise and outliers. We achieve this by using the discrete median rather than the mean as the estimator of the center of a cluster. Comparison with k-Means, Expectation Maximization and Gibbs sampling demonstrates the advantages of our algorithm.