A framework to monitor clusters evolution applied to economy and finance problems

作者：Oliveira Marcia^*; Gama Joao

来源：Intelligent Data Analysis, 2012, 16(1): 93-111.

DOI：10.3233/IDA-2011-0512

• Bipartite graphs
• change mining
• clustering evolution
• monitoring
• transitions

The study of evolution has become an important research issue, especially in the last decade, due to our ability to collect and store high detailed and time-stamped data. The need for describing and understanding the behavior of a given phenomena over time led to the emergence of new frameworks and methods focused on the temporal evolution of data and models. In this paper we address the problem of monitoring the evolution of clusters over time and propose the MEC framework. MEC traces evolution through the detection and categorization of clusters transitions, such as births, deaths and merges, and enables their visualization through bipartite graphs. It includes a taxonomy of transitions, a tracking method based in the computation of conditional probabilities, and a transition detection algorithm. We use MEC with two main goals: to determine the general evolution trends and to detect abnormal behavior or rare events. To demonstrate the applicability of our framework we present real world economic and financial case studies, using datasets extracted from Banco de Portugal Central Balance-Sheet Database and the The Data Page of New York University -Leonard N. Stern School of Business. The results allow us to draw interesting conclusions about the evolution of activity sectors and European companies.

• 出版日期2012