Chameleon 2 An Improved Graph-Based Clustering Algorithm

• Published in: ACM transactions on knowledge discovery from data Vol. 13; no. 1; pp. 1 - 27
• Main Authors: Barton, Tomas, Bruna, Tomas, Kordik, Pavel
• Format: Journal Article
• Language: English
• Published: 01.01.2019
• ISSN: 1556-4681; 1556-472X
• DOI: 10.1145/3299876

Traditional clustering algorithms fail to produce human-like results when confronted with data of variable density, complex distributions, or in the presence of noise. We propose an improved graph-based clustering algorithm called Chameleon 2, which overcomes several drawbacks of state-of-the-art clustering approaches. We modified the internal cluster quality measure and added an extra step to ensure algorithm robustness. Our results reveal a significant positive impact on the clustering quality measured by Normalized Mutual Information on 32 artificial datasets used in the clustering literature. This significant improvement is also confirmed on real-world datasets. The performance of clustering algorithms such as DBSCAN is extremely parameter sensitive, and exhaustive manual parameter tuning is necessary to obtain a meaningful result. All hierarchical clustering methods are very sensitive to cutoff selection, and a human expert is often required to find the true cutoff for each clustering result. We present an automated cutoff selection method that enables the Chameleon 2 algorithm to generate high-quality clustering in autonomous mode.