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.
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Araújo, D., Neto, A.D., Martins, A. (2012). Comparative Study on Information Theoretic Clustering and Classical Clustering Algorithms. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_57
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DOI: https://doi.org/10.1007/978-3-642-33266-1_57
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