Dissimilarity Clustering by Hierarchical Multi-Level Refinement
arXiv:1204.6509v15 citations
Originality Synthesis-oriented
AI Analysis
This work addresses clustering for data with dissimilarity measures, but it appears incremental as it builds on hierarchical clustering and heuristic refinement.
The paper tackles the problem of clustering dissimilarity data by optimizing the quantization error, achieving better quantization errors than existing methods.
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