Association rule mining is a well-known technique in data mining. Classification using association rules combines association rule mining and classification, and is therefore concerned with finding rules that accurately predict a single target (class) variable. The key strength of association rule mining is that all interesting rules are found. The number of associations present in even moderate sized databases can be, however, very large-usually too large to be applied directly for classification purposes. This project compares and combines different approaches for classification using association rules. This research area is called classification using association rules. An important aspect of classification using association rules is that it can provide quality measures for the output of the underlying mining process. The properties of the resulting classifier can be the base for comparisons between different association rule mining algorithms. A certain mining algorithm is preferable when the mined rule set forms a more accurate, compact and stable classifier in an efficient way. First, in this project we are interested in the comparison of the quality of different mining algorithms. Therefore, we use classification using association rules. Secondly, classification using association rules can be improved itself by using a mining algorithm that prefers highly accurate rules. The author of the report is indebted to several students and research assistants who showed interest and got involved in the work.