A fuzzy approach towards inferential data mining

Data mining can benefit from fuzzy techniques to order an otherwise intractable search. This paper develops a fuzzy logic for rule discovery and inference with application to decision support systems. Data mining traditionally addresses the randomization of numerical data. It is not only clear that such mining operations can be readily extended to symbolic data, but that this then implies that two other results will follow. First, symbolic data can take the form of natural language in supervised or unsupervised learning; and second, randomization can take the form of rules for use in an expert system. It will be argued that the knowledge acquisition bottleneck can only be cracked if expert systems are bootstrapped using natural language.