A vector space model for automatic indexing
Communications of the ACM
Algorithm 447: efficient algorithms for graph manipulation
Communications of the ACM
The Journal of Machine Learning Research
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A possibilistic approach to string comparison
IEEE Transactions on Fuzzy Systems
Properties of possibilistic string comparison
IEEE Transactions on Fuzzy Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
Hi-index | 0.00 |
The ongoing exponential growth of online information sources has led to a need for reliable and efficient algorithms for text clustering. In this paper, we propose a novel text model called the relational text model that represents each sentence as a binary multirelation over a concept space \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{C}}$\end{document} **image** . Through usage of the smart indexing engine (SIE), a patented technology of the Belgian company i.Know, the concept space adopted by the text model can be constructed dynamically. This means that there is no need for an a priori knowledge base such as an ontology, which makes our approach context independent. The concepts resulting from SIE possess the property that frequency of concepts is a measure for relevance. We exploit this property with the development of the CR-algorithm. Our approach relies on the representation of a data set \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{D}}$\end{document} **image** as a multirelation, of which k-cuts can be taken. These cuts can be seen as sets of relevant patterns with respect to the topics that are described by documents. Analysis of dependencies between patterns allows to produce clusters, such that precision is sufficiently high. The best k-cut is the one that best approximates the estimated number of clusters to ensure recall. Experimental results on Dutch news fragments show that our approach outperforms both basic and advanced methods. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.