Clustering Algorithms Optimizer: A Framework for Large Datasets

Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are routinely applied, many of them suffer from the following limitations: (i) relying on predetermined parameters tuning, such as a-priori knowledge regarding the number of clusters; (ii) involving nondeterministic procedures that yield inconsistent outcomes. Thus, a framework that addresses these shortcomings is desirable. We provide a datadriven framework that includes two interrelated steps. The first one is SVDbased dimension reduction and the second is an automated tuning of the algorithm’s parameter(s). The dimension reduction step is efficiently adjusted for very large datasets. The optimal parameter setting is identified according to the internal evaluation criterion known as Bayesian Information Criterion (BIC). This framework can incorporate most clustering algorithms and improve their performance. In...

• Type: Conference
• Year: 2007
• Where: ISBRA
• Authors: Roy Varshavsky, David Horn, Michal Linial
• Keywords: Algorithms, Bioinformatics, Dimension Reduction, ISBRA 2007, Quantum Clustering Algorithms