Dissimilarity Clustering by Hierarchical Multi-Level Refinement
Machine Learning
2012-05-02 v1 Machine Learning
Abstract
We introduce in this paper a new way of optimizing the natural extension of the quantization error using in k-means clustering to dissimilarity data. The proposed method is based on hierarchical clustering analysis combined with multi-level heuristic refinement. The method is computationally efficient and achieves better quantization errors than the
Keywords
Cite
@article{arxiv.1204.6509,
title = {Dissimilarity Clustering by Hierarchical Multi-Level Refinement},
author = {Brieuc Conan-Guez and Fabrice Rossi},
journal= {arXiv preprint arXiv:1204.6509},
year = {2012}
}
Comments
20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012)