A novel clustering approach for biological data using a new distance based on Gene Ontology

When applying clustering algorithms on biological data the information about biological processes is not usually present in an explicit way, although this knowledge is later used by biologists to validate the clusters and the relations found among data. This work presents a new distance measure for biological data which combines expression and semantic information, in order to be used into a clustering algorithm. The distance is calculated pairwise among all pairs of genes and it is incorporated during the training process of the clustering algorithm. The approach was evaluated on two real datasets using several validation measures. The obtained results are consistent across all the measures, showing better semantic quality for clusters with the new algorithm in comparison to standard clustering.Sociedad Argentina de Informática e Investigación Operativ

A novel clustering approach for biological data using a new distance based on Gene Ontology

When applying clustering algorithms on biological data the information about biological processes is not usually present in an explicit way, although this knowledge is later used by biologists to validate the clusters and the relations found among data. This work presents a new distance measure for biological data which combines expression and semantic information, in order to be used into a clustering algorithm. The distance is calculated pairwise among all pairs of genes and it is incorporated during the training process of the clustering algorithm. The approach was evaluated on two real datasets using several validation measures. The obtained results are consistent across all the measures, showing better semantic quality for clusters with the new algorithm in comparison to standard clustering.Sociedad Argentina de Informática e Investigación Operativ

A novel path-based clustering algorithm using multi-dimensional scaling

Data clustering is a difficult and challenging task, especially when the hidden clusters are of different shapes and non-linearly separable in the input space. This paper addresses this problem by proposing a new method that combines a path-based dissimilarity measure and multi-dimensional scaling to effectively identify these complex separable structures. We show that our algorithm is able to identify clearly separable clusters of any shape or structure. Thus showing that our algorithm produces model clusters; that follow the definition of a cluster