An Algorithm of granulation on numeric attributes for association rules mining

Mining association rules from numeric data is relatively more difficult than categorical data. The main reason is that the domain of real number lacks of the user's abstraction on reality. In this paper, we propose an algorithm to granualte numeric intervals automatically. The proposed method defines two threshold factors, information density-similarity and information closeness, to measure the condition if two granules should be merged and construct an abstraction hierarchy of intervals. For abstracting the best level of interval from the interval hierarchy automatically, we develop a determination function based on the threshold factors. After the intervals are determined, the fuzzy membership functions for each interval can be generated.Then an algorithm for mining fuzzy association rules can be used mine qualified association rules from the fuzzy intervals.