Generating classification association rules with modified Apriori algorithm
Birkan Tunç, Hasan Dağ · 2006
Abstract:- Association rule mining is a useful and widely used method to extract patterns from large sets of data especially if we deal with basket type data such as customer buying attitudes. It finds all possible relations between data fields, and this property is functional in many research domains; however it happens to be useless in domains like medicine and health as it produce lots of ineffectual rules concerning irrelevant data fields. Thus it is not likely to classify a disease easily by using classic rule mining algorithms. Classification algorithms, on the other hand, only generate decision trees or classifiers according to pre-determined target; therefore, they need to be tuned to produce human readable rules that can be used in decision support. In this study, an integrated approach was proposed to produce association rules that can be used as classifiers. Apriori algorithm was used as a base model and modified the algorithm to be able to generate human readable classification association rules. Some experiments with real medical data sets were conducted to compare our results with the results of other well known algorithms like C4.5 and Ripper.