Effective solutions for Web search engines can take advantage of algorithms for the automatic organization of documents into homogeneous clusters. Unfortunately, document clustering is not an easy task especially when the documents share a common set of topics, like in vertical search engines. In this paper we propose two clustering algorithms which can be tuned by the feedback of an expert. The feedback is used to choose an appropriate basis for the representation of documents, while the clustering is performed in the projected space. The algorithms are evaluated on a dataset containing papers from computer science conferences. The results show that an appropriate choice of the representation basis can yield better performance with respect to the original vector space model.
Pseudo-Supervised Clustering for Text Documents
Turchi, Marco
2004-01-01
Abstract
Effective solutions for Web search engines can take advantage of algorithms for the automatic organization of documents into homogeneous clusters. Unfortunately, document clustering is not an easy task especially when the documents share a common set of topics, like in vertical search engines. In this paper we propose two clustering algorithms which can be tuned by the feedback of an expert. The feedback is used to choose an appropriate basis for the representation of documents, while the clustering is performed in the projected space. The algorithms are evaluated on a dataset containing papers from computer science conferences. The results show that an appropriate choice of the representation basis can yield better performance with respect to the original vector space model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
