One of the main tasks of Text Mining is organising a large number of unlabelled documents into a smaller set of meaningful and coherent clusters, similar with respect to their content. Clustering algorithms are usually carried on documents × terms matrices, algebraically representing each document as a vector. Nevertheless, a collection of documents can also be encoded differently, e.g. by considering a documents × documents representation. This peculiar data structure can be seen as an adjacency matrix and graphically displayed as a graph. In the frame- work of Network Analysis, community detection is performed on such graphs to find groups of nodes sharing common characteristics, and play similar roles. This paper aims at evaluating the use of different data structures and different grouping criteria, showing the effectiveness of the different alternatives in a text categorisation strategy. We performed a comparative study involving both classical text clustering approaches and community detection approaches, testing and discussing their performances.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
|Titolo:||A comparative study on community detection and clustering algorithms for text categorisation|
MISURACA, Michelangelo (Corresponding)
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|