Clustering Documents Using Tagging Communities and Semantic Proximity

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

Authors

Elisabete Cunha (Author) Other; Alvaro Figueira (Author) FCUP; Oscar Mealha (Author) Other

Conference proceedings International

Title: PROCEEDINGS OF THE 2013 8TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI 2013)

8th Iberian Conference on Information Systems and Technologies (CISTI)

Lisboa, PORTUGAL, JUN 19-22, 2013

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-008-GWC

Abstract (EN): Euclidean distance and cosine similarity are frequently used measures to implement the k-means clustering algorithm. The cosine similarity is widely used because of it's independence from document length, allowing the identification of patterns, more specifically, two documents can be seen as identical if they share the same words but have different frequencies. However, during each clustering iteration new centroids are still computed following Euclidean distance. Based on a consideration of these two measures we propose the k-Communities clustering algorithm (k-C) which changes the computing of new centroids when using cosine similarity. It begins by selecting the seeds considering a network of tags where a community detection algorithm has been implemented. Each seed is the document which has the greater degree inside its community. The experimental results found through implementing external evaluation measures show that the k-C algorithm is more effective than both the k-means and k-means++. Besides, we implemented all the external evaluation measures, using both a manual and an automatic "Ground Truth", and the results show a great correlation which is a strong indicator that it is possible to perform tests with this kind of measures even if the dataset structure is unknown.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6