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The article discusses various reports published within the issue, including one on an approach to combine objectivity with modeling process to monitor the frequency behavior of item-sets and another on a model fitness evaluation method to identify positive and negative instances effectively.

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data. [ABSTRACT FROM AUTHOR]