An extensive comparative study of cluster validity indices

Authors:
Olatz Arbelaitz;Ibai Gurrutxaga;Javier Muguerza;JesúS M. PéRez;IñIgo Perona
Affiliations:
Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU, Manuel Lardizabal 1, 20018 Donostia, Spain
Venue:
Pattern Recognition
Year:
2013

Citing 26
Cited 0

Bootstrap technique in cluster analysis

Pattern Recognition
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Journal of Computational and Applied Mathematics
How many clusters are best?—an experiment

Pattern Recognition
Algorithms for clustering data

Algorithms for clustering data
On the number of clusters

Computational Statistics & Data Analysis - Special issue on classification
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Applications of Data Mining in Computer Security

Applications of Data Mining in Computer Security
On Clustering Validation Techniques

Journal of Intelligent Information Systems
Performance Evaluation of Some Clustering Algorithms and Validity Indices

IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A new cluster validity measure and its application to image compression

Pattern Analysis & Applications
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)

Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
New indices for cluster validity assessment

Pattern Recognition Letters
Model-based evaluation of clustering validation measures

Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
A density-based cluster validity approach using multi-representatives

Pattern Recognition Letters
A Bounded Index for Cluster Validity

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters

IEEE Transactions on Knowledge and Data Engineering
Characterization and evaluation of similarity measures for pairs of clusterings

Knowledge and Information Systems
Performance evaluation of some symmetry-based cluster validity indexes

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Normality-based validation for crisp clustering

Pattern Recognition
SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index

Pattern Recognition
Validity index for clusters of different sizes and densities

Pattern Recognition Letters
Towards a standard methodology to evaluate internal cluster validity indices

Pattern Recognition Letters
A Cluster Separation Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence
Some new indexes of cluster validity

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.01

Visualization

Abstract

The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics. These results can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices.