In this paper, we propose a new approach to automatically clustering e-commerce search engines (ESEs) on the Web such that ESEs in the same cluster sell similar products. This all...
Unsupervised learning methods often involve summarizing the data using a small number of parameters. In certain domains, only a small subset of the available data is relevant for ...
We present a novel approach to managing redundancy in sequence databanks such as GenBank. We store clusters of near-identical sequences as a representative union-sequence and a se...
Michael Cameron, Yaniv Bernstein, Hugh E. Williams
The ambiguity of person names in the Web has become a new area of interest for NLP researchers. This challenging problem has been formulated as the task of clustering Web search r...
We initiate a novel study of clustering problems. Rather than specifying an explicit objective function to optimize, our framework allows the user of clustering algorithm to speci...