Rule-based systems for classification have the disadvantage that they involve exact values for continuous attributes. Fuzzy logic is useful for data mining systems performing classification. It provides the benefit of working at a high level of abstraction. In general, the usage of fuzzy logic in rule-based systems involves the following:
- Attribute values are changed to fuzzy values.
- For a given new sample, more than one fuzzy rule may apply. Every applicable rule contributes a vote for membership in the categories. Typically, the truth values for each projected category are summed.
- The sums obtained above are combined into a value that is returned by the system. This process may be done by weighting each category by its truth sum and multiplying by the mean truth value of each category. The calculations involved may be more complex, depending on the difficulty of the fuzzy membership graphs.
