On post-clustering evaluation and modification

摘要

Unsupervised clustering algorithms sometimes do not lead to meaningful interpretations of the structure in the data. We propose a new approach in which the concept of cluster density is introduced to assess the quality of an algo- rithmically generated partition and accordingly guide an amelioration process through split-and-merge operations.