Hierarchical clustering with high order dissimilarities

Authors:
Helena Aidos;Ana Fred
Affiliations:
Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal;Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
Venue:
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Year:
2011

Citing 5
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Cluster Isolation Criterion Based on Dissimilarity Increments

IEEE Transactions on Pattern Analysis and Machine Intelligence
SMEM Algorithm for Mixture Models

Neural Computation
On the distribution of dissimilarity increments

IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel hierarchical clustering algorithm based on high order dissimilarities. These dissimilarity increments are measures computed over triplets of nearest neighbor points. Recently, the distribution of these dissimilarity increments was derived analytically. We propose to incorporate this distribution in a hierarchical clustering algorithm to decide whether two clusters should be merged or not. The proposed algorithm is parameter-free and can identify classes as the union of clusters following the dissimilarity increments distribution. Experimental results show that the proposed algorithm has excellent performance over well separated clusters, also providing a good hierarchical structure insight into touching clusters.