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System to build fuzzy logic models from databases and application to multisensor data

This paper presents the results of a trial application of a data mining algorithm to a multi-sensor data base. The data base structure is assumed to consist of a single dependent variable and an unspecified number of independent variables (if the data base contains more than one dependent variable, a separate model is built for each dependent variable). Given a set of values for the independent variables, the fuzzy logic model estimates the value of the dependent variable, and the error in the estimated value. The algorithm re-organizes the data records into a multi- dimensional partition tree. The tree is binary (each node is partitioned into exactly two disjoint nodes) and unbalanced (the two child nodes do not have the same number of members). The partitioning algorithm is greedy: each node is partitioned independently, and at each node the algorithm searches to find the independent variable and partition threshold that best accounts for variance in the dependent variable. Partitioning stops when the variance in the dependent variable at a node is less than some user- specified threshold. Fuzzy logic rules are constructed from the leaf nodes. The algorithm compresses the data base into a set of fuzzy logic rules. The set of fuzzy logic rules is a model of the information in the data base. We conducted meta-analysis of the relationships between the independent and dependent variables by studying how the independent variables are used in the fuzzy logic model, i.e., the number of rules that use each independent variable and the number of data records in the training data to which those rules apply.