Comparative study on information theoretic clustering and classical clustering algorithms

Authors:
Daniel Araújo;Adrião Dória Neto;Allan Martins
Affiliations:
Federal Rural University of Semi-Árido, Angicos, RN, Brasil,Departament of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN, Brasil;Departament of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN, Brasil;Departament of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN, Brasil
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

Citing 6
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Abstract

This paper proposes a comparative empirical study on algorithms for clustering. We tested the method proposed in [2] using distinct synthetic and real (gene expression) datasets. We chose synthetic datasets with different spatial complex to verify the applicability of the algorithm. We also evaluated the IT algorithm in real-life problems by using microarray gene expression datasets. Compared with simple but still spread used classical algorithms k-means, hierarchical clustering and finite mixture of Gaussians, the IT algorithm showed to be more robust for both proposed scenarios.