Partition selection approach for hierarchical clustering based on clustering ensemble

Authors:
Sandro Vega-Pons;José Ruiz-Shulcloper
Affiliations:
Advanced Technologies Application Center, Havana, Cuba;Advanced Technologies Application Center, Havana, Cuba
Venue:
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Year:
2010

Citing 7
Cited 1

Data clustering: a review

ACM Computing Surveys (CSUR)
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering

Clustering
Cluster Analysis

Cluster Analysis
Weighted partition consensus via kernels

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

Pattern Recognition

Weighted association based methods for the combination of heterogeneous partitions

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hierarchical clustering algorithms are widely used in many fields of investigation. They provide a hierarchy of partitions of the same dataset. However, in many practical problems, the selection of a representative level (partition) in the hierarchy is needed. The classical approach to do so is by using a cluster validity index to select the best partition according to the criterion imposed by this index. In this paper, we present a new approach based on the clustering ensemble philosophy. The representative level is defined here as the consensus partition in the hierarchy. In the consensus computation process, we take into account the similarity between partitions and information from the evaluation of partitions with different cluster validity indexes. An experimental comparison on several datasets shows the superiority of the proposed approach with respect to the classical approach.