Linguistic summarization using IF-THEN rules

Linguistic summarization (LS) is a data mining or knowledge discovery approach to extract patterns from databases. It has been studied by many researchers; however, none of them has used it to generate IF-THEN rules, which can be added to a knowledge base for better understanding of the data, or be used in Perceptual Reasoning to infer the outputs for new scenarios. In this paper LS using IF-THEN rules is proposed. Five quality measures for such summaries are defined. Among them, the degree of usefulness is especially valuable for finding the most reliable and representative rules, and the degree of outlier can be used to identify outlier rules and data. An example verifies the effectiveness of our approach. The relationship between LS and the Wang-Mendel method is also discussed.