caa a22 4500 463251458 CHVBK 20180405153341.0 cr unu---uuuuu 170326e20071201xx s 000 0 eng 10.1007/s10791-007-9035-7 doi (NATIONALLICENCE)springer-10.1007/s10791-007-9035-7 Reforgiato Recupero Diego Dipartimento di Matematica e Informatica, Università degli Studi di Catania, Catania, Italy aut A new unsupervised method for document clustering by using WordNet lexical and conceptual relations [Elektronische Daten] [Diego Reforgiato Recupero] Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and speed. Springer Science+Business Media, LLC, 2007 Clustering nationallicence Text documents nationallicence Bisecting k -means nationallicence Multipole nationallicence Antipole nationallicence WordNet nationallicence Information Retrieval Springer Netherlands 10/6(2007-12-01), 563-579 1386-4564 10:6<563 2007 10 10791 https://doi.org/10.1007/s10791-007-9035-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10791-007-9035-7 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Reforgiato Recupero Diego Dipartimento di Matematica e Informatica, Università degli Studi di Catania, Catania, Italy aut NATIONALLICENCE 773 0- Information Retrieval Springer Netherlands 10/6(2007-12-01), 563-579 1386-4564 10:6<563 2007 10 10791 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer