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Volume XIV, Number 1. May 2009

High accuracy method for discovering quantitave association rules in datatables and databases

A. Shragai. Tel Aviv University

D.E. Tamir. Texas State University

M. Schneider. Natanya College

A. Kandel. University of South Florida

This paper introduces a new and novel deterministic technique for mining association rules from quantitative data tables and databases and show how to use these techniques to devise a fuzzy-inference based apriori algorithm for discovering associations. The algorithm is sound and efficient. It introduces a complexity level that is equivalent…
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An analysis of monthly effects in the spanish stock market using artificial neural networks

M. Teresa Sorrosal Forradellas. Rovira i Virgili University

Dídac Ramírez Sarrió. University of Barcelona

This paper presents an analysis of the monthly effects in the Spanish index IBEX-35. Alternatively to other approaches, this work uses a kind of artificial neural networks, the Self Organizing Maps, in order to detect significant differences in the behaviour of the value of the IBEX-35 due to a particular…
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A time series knowledge mining framework exploiting the synergy between subsequence clustering and predictive Markovian models

Vasile Georgescu. University of Craiova

This paper proposes a time series knowledge mining framework, designed to favor the synergy between subsequence time series clustering and predictive tools such as Hidden Markov Models. Many tasks for temporal data mining rely heavily on the choice of the representation scheme and the dissimilarity measure. The first part is…
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Computing ambiguity in complex systems with Fuzzy logic

Luca Iandoli, Elio Marchione, Cristina Ponsiglione, Giuseppe Zollo. Università degli Studi di Napoli Federico II

This paper proposes a modelization of complex social systems based on the integration of two computational methodologies: fuzzy logic and agent-based modelling. In particular, the objective of this work is to present a methodology to take into account the ambiguity of verbal interactions in learning processes. To this aim, we…
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