A scalable hierarchical fuzzy clustering algorithm for text mining

• Type: Article in International Conference Proceedings Book
• Date: 2004

Authors

Maria Eduarda Silva Mendes Rodrigues (Author) FEUP; L. Sacks (Author) Other

Conference proceedings International

Title: 4th International Conference on Recent Advances in Soft Computing, RASC'2004

Pages: 1-6

Nottingham, UK, December 2004

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Abstract (EN): Clustering techniques are generally applied for finding unobvious relations and structures in data sets. In this paper, we propose a novel scalable hierarchical fuzzy clustering algorithm to discover relationships between information resources based on their textual content, as well as to represent knowledge through the association of topics covered by those resources. The algorithm addresses the important problem of defining a suitable number of clusters for appropriately capturing all the topics of the knowledge domain. In particular, the sought granularity level defines the number of clusters. Furthermore, the algorithm exploits the concept of asymmetric similarity to link clusters hierarchically and to form a topic hierarchy

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6