CCAR: An efficient method for mining class association rules with itemset constraints

摘要: Abstract Class association rules (CARs) are basically used to build a classification model for prediction; they can also be describe correlations between itemsets and class labels. The latter is very popular in mining medical data. For example, epidemiologists often consider which indicate the relations risk factors (itemsets) HIV test results (class labels). However, real world, end users interested subset of rules. Particularly, may only contain at least one itemset from user-defined set rule antecedent. when classifying populations high infection, concentrate on that include demographic information such as sex, age, marital status antecedents. Two naive strategies solve this problem by applying constraints into pre-processing or post-processing step. approaches time-intensive. This paper thus proposes an efficient method integrating process. experimental show proposed algorithm outperforms two basic time memory consumption. practical benefits our demonstrated real-life application HIV/AIDS domain.