Bipartite Graphs for Monitoring Clusters Transitions

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

Oliveira, M (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: ADVANCES IN INTELLIGENT DATA ANALYSIS IX, PROCEEDINGS

Pages: 114-124

9th International Symposium on Intelligent Data Analysis

Tucson, AZ, MAY 19-21, 2010

Indexing

ISI Web of Knowledge - 15 Citations

Scopus - 18 Citations

Other information

Authenticus ID: P-003-9ZZ

DOI: 10.1007/978-3-642-13062-5_12

Abstract (EN): The study of evolution has become an important research issue; especially in the last decade, due to a greater awareness of our world's volatility. As a consequence, a new paradigm has emerged to respond more effectively to a elms of new problems in Data Mining. In this paper we address the problem of monitoring the evolution of clusters and propose the MClusT framework, which was developed along the lines of this new Change Mining paradigm. MClusT includes a taxonomy of transitions, a tracking method based in Graph Theory; and a transition detection algorithm. To demonstrate its feasibility and applicability we present; real world case studies, using datasets extracted from Banco de Portugal and the Portuguese Institute of Statistics. We also test our approach in a benchmark dataset from TSDL. The results are encouraging and demonstrate the ability of MClusT framework to provide an efficient diagnosis of clusters transitions.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11