Efficient Mining of Frequent Item Sets in Heterogeneous Data

Association rule mining has recently become a popular area of research. The most expensive stepof discovering association rules is to find so-called frequent item sets. The focus of this paper isefficient mining of frequent item sets when the input data contains categorical and quantitativeattributes.We propose a new Apriori-like algorithm to solve this problem. The new algorithm, that we havecalled Gradual Apriori, generates about 30% less candidates than the traditional algorithm. More...