A Comparison Between Rule Based and Association Rule Mining Algorithms

• Main Authors: Mazid, M.M., Ali, A.B.M.S., Tickle, K.S.
• Format: Conference Proceeding
• Language: English
• Subjects: Apriori; Association Rule Mining; Association rules; Classification; Classification algorithms; Classification tree analysis; Computational complexity; DARPA (Defense Advanced Research Projects Agency); Data mining; Decision trees; Intrusion detection; Partial Decision Tree (PART); Supervised learning; Testing; Unsupervised learning
• DOI: 10.1109/NSS.2009.81

Recently association rule mining algorithms are using to solve data mining problem in a popular manner. Rule based mining can be performed through either supervised learning or unsupervised learning techniques. Among the wide range of available approaches, it is always challenging to select the optimum algorithm for rule based mining task. The aim of this research is to compare the performance between the rule based classification and association rule mining algorithm based on their rule based classification performance and computational complexity. We consider PART (Partial Decision Tree) of classification algorithm and Apriori of association rule mining to compare their performance. DARPA (Defense Advanced Research Projects Agency) data is a well-known intrusion detection problem is also used to measure the performance of these two algorithms. In this comparison the training rules are compared with the predefined test sets. In terms of accuracy and computational complexity we observe Apriori is a better choice for rule based mining task.