Internal clustering evaluation of data streams

Abstract

Clustering validation is a crucial part of choosing a clustering algorithm which performs best for an input data. Internal clustering validation is efficient and realistic, whereas external validation requires a ground truth which is not provided in most applications. In this paper, we analyze the properties and performances of eleven internal clustering measures. In particular, as the importance of streaming data grows, we apply these measures to carefully synthesized stream scenarios to reveal how they react to clusterings on evolving data streams. A series of experimental results show that different from the case with static data, the Calinski-Harabasz index performs the best in coping with common aspects and errors of stream clustering.