期刊
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
卷 93, 期 5, 页码 807-820出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207160.2014.898065
关键词
97R40; 91G80; 68U35; 68T10; 62H30; traffic lights panel; financial assets; patterns interpretation; clustering; association rules; Knowledge Discovery in Databases; post-processing; general linear model; Venezuela Stock Exchange; frequent itemsets; data mining
In last years, mining financial data has taken remarkable importance to complement classical techniques. Knowledge Discovery in Databases provides a framework to support analysis and decision-making regarding complex phenomena. Here, clustering is used to mine financial patterns from Venezuelan Stock Exchange assets (Bolsa de Valores de Caracas), and two major indexes related to that market: Dow Jones (USA) and BOVESPA (Brazil). Also, from a practical point of view, understanding clusters is crucial to support further decision-making. Only few works addressed bridging the existing gap between the raw data mining (DM) results and effective decision-making. Traffic lights panel (TLP) is proposed as a post-processing tool for this purpose. Comparison with other popular DM techniques in financial data, like association rules mining, is discussed. The information learned with the TLP improves quality of predictive modelling when the knowledge discovered in the TLP is used over a multiplicative model including interactions.
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