In this work we investigate the feasibility and effectiveness of unsupervised tissue clustering and classification algorithms for DTI data. Tissue clustering and classification ...
— We study the problem of building an optimal network-layer clustering hierarchy, where the optimality can be defined using three potentially conflicting metrics: state, delay ...
Leonid B. Poutievski, Kenneth L. Calvert, Jim Grif...
Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1, which uses explicitly modeling of the s...
Clustering is one solution to the demand for wideissue machines and fast clock cycles because it allows for smaller, less ported register files and simpler bypass logic while rema...
Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...