Concept Classification Using a Hybrid Data Mining Model

Apriori is a well-known algorithm which is used extensively in market-basket analysis and data mining. The algorithm is used for learning association rules from transactional databases and is based on simple counting procedures. In this paper we propose enhancements to Apriori which allow it to perform concept classification similar to the way decision tree algorithms learn. Specifically, training examples are modified and treated as transactional data and the results are verified and further improved by C4.5 decision tree and k-means clustering algorithms, respectively. To demonstrate the novelty of the enhanced Apriori algorithm, we present a hybrid data mining model (HDMM) which identifies at-risk students based on their academic performance and other pertinent data.