Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mi...
Piotr Berman, Bhaskar DasGupta, Ming-Yang Kao, Jie...
We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common i...
Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) simi...
This paper presents a new learning method for automatic acquisition of translation knowledge from parallel corpora. We apply this learning method to automatic extraction of bilingu...
Document clustering is useful in many information retrieval tasks: document browsing, organization and viewing of retrieval results, generation of Yahoo-like hierarchies of docume...