Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

Vinayaka : a Semi-supervised Projectedclusteringmethodusing Differential Evolution

Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have been solved by using DE based clustering methods but these methods may fail to find clusters hidden in subspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed in literature to find subspace clusters that are present in subspaces of dataset. In this pap...

Semi-Supervised Projected Clustering

Recent studies suggest that projected clusters with extremely low dimensionality exist in many real datasets. A number of projected clustering algorithms have been proposed in the past several years, but few can identify clusters with dimensionality lower than 10% of the total number of dimensions, which are commonly found in some real datasets such as gene expression profiles. In this paper we...

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

OBJECTIVES A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric prope...

Incremental Semi-supervised Clustering Method Using Neighbourhood Assignment

Semi-supervised considering so as to cluster expects to enhance clustering execution client supervision as pair wise imperatives. In this paper, we contemplate the dynamic learning issue of selecting pair wise must-connect and can't interface imperatives for semi supervised clustering. We consider dynamic learning in an iterative way where in every emphasis questions are chosen in light of the ...

Semi-supervised Clustering Using Heterogeneous Dissimilarities