Naive-Bayes Classification using Fuzzy Approach

Data Mining is the quest for knowledge in databases to uncover previously unimagined relationships in the data. This paper generalizes Naive-Bayes classification technique using fuzzy set theory, when the available numerical probabilistic information is incomplete or partially correct. We consider a training dataset, where attribute values have certain similarities in nature. Though nothing can replace precise and complete probabilistic information, a useful classification system for data mining can be built even with imperfect data by introducing domain-dependent constraints. This observation is analyzed here based on fuzzy proximity relations for the domain of each attribute. The study shows that this approach is highly suitable for real-world applications, especially when databases contain uncertain information.