F-statistics algorithm for gene clustering evaluation
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Robust data clustering by learning multi-metric Lq-norm distances
Expert Systems with Applications: An International Journal
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This paper compares the performance of three clustering algorithms on the task of outlier's detection. The goal is to illustrate that better clustering indicates better detection of outliers. k-means (KM), Bisecting k-means (BKM) and the Partitioning Around Medoids (PAM) algorithms are each combined with the clustering-based outliers detection (Find CBLOF) method. Undertaken experimental results over four gene expression datasets where outliers are presented show that the clustering solutions of the PAM algorithm enable the Find CBLOF algorithm to discover more outliers than those of both the k-means and the bisecting k-means algorithms. The main reason for this is that PAM provides better clustering quality than that of the other two clustering algorithms on the tested datasets measured by external and internal quality measures.