Clustering and Classifying Text Documents - A Revisit to Tagging Integration Methods

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Cunha, E (Author) Other; Figueira, A (Author) FCUP; Mealha, O (Author) Other

Conference proceedings International

Title: IC3K 2013; KDIR 2013 - 5th International Conference on Knowledge Discovery and Information Retrieval and KMIS 2013 - 5th International Conference on Knowledge Management and Information Sharing, Proc.

Pages: 160-168

5th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2013 and the 5th International Conference on Knowledge Management and Information Sharing, KMIS 2013

Vilamoura, Algarve, 19 September 2013 through 22 September 2013

Indexing

ISI Web of Knowledge

Scopus - 1 Citation

Other information

Authenticus ID: P-008-GVR

DOI: 10.5220/0004545201600168

Abstract (EN): In this paper we analyze and discuss two methods that are based on the traditional k-means for document clustering and that feature integration of social tags in the process. The first one allows the integration of tags directly into a Vector Space Model, and the second one proposes the integration of tags in order to select the initial seeds. We created a predictive model for the impact of the tags' integration in both models, and compared the two methods using the traditional k-means++ and the novel k-C algorithm. To compare the results, we propose a new internal measure, allowing the computation of the cluster compactness. The experimental results indicate that the careful selection of seeds on the k-C algorithm present better results to those obtained with the k-means++, with and without integration of tags.

Language: English

Type (Professor's evaluation): Scientific