Model Identification with Fuzzy-Optimisation Techniques in Geological Data Mining

Abstract

Abstract Zadeh's proposal of modelling the mechanism of human thinking with linguistic values rather than ordinary (crisp) numbers led to the introduction of fuzziness into statistical and dynamical modelling and to the development of a new class of systems called fuzzy models. In fuzzy modelling, one of the most important problems is the identification of a predictive model from a set of measurements. In a similar way, Data Mining is aimed at sieving data from large databases, data repositories or data warehouses in order to discover interesting knowledge such as pattern associations, trends, and significant structures. Therefore, many similarities can be found between data mining and fuzzy model identification, specially when one has to deal with imprecision and noise in very large data sets. Two of these similarities correspond with the inverse problem of variable selection and parameter identification in order to select the most appropriate predictive model that fits a set of observed (training) data. In this paper we introduce a data mining procedure which uses global optimisation methods, that seeks for useful features, pattern relations, and functional representations in order to derive fuzzy models from large data sets, that characterise the unknown functions as precise as possible. The effectiveness of the proposed data mining method is proved using petrophysical data (core plug measurements) from two oil wells in the Maracaibo Basin.