Internal clustering evaluation of data streams

2Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Hassani, M., & Seidl, T. (2015). Internal clustering evaluation of data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9441, pp. 198–209). Springer Verlag. https://doi.org/10.1007/978-3-319-25660-3_17

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free