Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semisupervised clustering algo...
Abstract. Clustering algorithms based on a matrix of pairwise similarities (kernel matrix) for the data are widely known and used, a particularly popular class being spectral clust...
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression pa...
In this paper, we investigate the maximization of the amount of gathered data in a clustered wireless sensor network (WSN). The amount of gathered data is maximized by (1) choosing...
—We consider approaches for similarity search in correlated, high-dimensional data-sets, which are derived within a clustering framework. We note that indexing by “vector appro...