The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mix...
Hierarchical clustering is used widely to organize data and search for patterns. Previous algorithms assume that the body of data being clustered is fixed while the algorithm runs...
H. Van Dyke Parunak, Richard Rohwer, Theodore C. B...
We address the problem of clustering of contour images from hardware tools based on string descriptions, in a comparative study of cluster combination techniques. Several clusteri...
The under-determined blind source separation (BSS) problem is usually solved using the sparse component analysis (SCA) technique. In SCA, the BSS is usually solved in two steps, w...
Abstract. In this paper, we propose a cluster-based cumulative representation for cluster ensembles. Cluster labels are mapped to incrementally accumulated clusters, and a matching...