In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
In this paper we propose a probabilistic model for online document clustering. We use non-parametric Dirichlet process prior to model the growing number of clusters, and use a pri...
In traditional text clustering methods, documents are represented as "bags of words" without considering the semantic information of each document. For instance, if two ...
Xiaohua Hu, Xiaodan Zhang, Caimei Lu, E. K. Park, ...
Several algorithms based on link analysis have been developed to measure the importance of nodes on a graph such as pages on the World Wide Web. PageRank and HITS are the most pop...
We propose a novel conception language for exploring the results retrieved by several internet search services (like search engines) that cluster retrieved documents. The goal is ...
Gloria Bordogna, Alessandro Campi, Giuseppe Psaila...