MEC - Monitoring Clusters' Transitions

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

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

Conference proceedings International

Title: STAIRS 2010: PROCEEDINGS OF THE FIFTH STARTING AI RESEARCHERS' SYMPOSIUM

Pages: 212-224

5th International Symposium for Artificial Intelligence Researchers (STAIRS)

Lisbon, PORTUGAL, AUG 16-20, 2010

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-006-604

DOI: 10.3233/978-1-60750-676-8-212

Abstract (EN): In this work we address the problem of monitoring the evolution of clusters, which became an important research issue in recent years due to our ability to collect and store data that evolves over time. The evolution is traced through the detection and categorization of transitions undergone by clusters' structures computed at different points in time. We adopt two main strategies for cluster characterization - representation by enumeration and representation by comprehension -, and propose the MEC (Monitor of the Evolution of Clusters) framework, which was developed along the lines of the change mining paradigm. MEC includes a taxonomy of various types of clusters' transitions, a tracking mechanism that depends on cluster representation, and a transition detection algorithm. Our tracking mechanism can be subdivided in two methods, devised to monitor clusters' transitions: one based on graph transitions, and another based on clusters' overlap. To demonstrate the feasibility and applicability of MEC we present real world case studies, using datasets from different knowledge areas, such as Economy and Education.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13