A method for design of data-tailored partitioning algorithms for optimizing the number of clusters in microarray analysis

We propose a method for designing a partitioning clustering algorithm from reusable components that is suitable for finding the appropriate number of clusters (K) in microarray data. The proposed method is evaluated on 10 datasets (4 syntetic and 6 real-word microarrays) by considering 1008 reusable-component-based algorithms and four normalization methods. The best performing algorithm were reported on every dataset and also rules were identified for designing microarray-specific clustering algorithms. The obtained results indicate that in the majority of cases a data-tailored clustering algorithm design outperforms the results reported in the literature. In addition, data normalization can have an important influence on algorithm performance. The method proposed in this paper gives insights for design of divisive clustering algorithms that can reveal the optimal K in a microarray dataset.