NDLI: A Comparison Between Rule Based and Association Rule Mining Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Mazid, M.M. Ali, A.B.M.S. Tickle, K.S.
Copyright Year	2009
Abstract	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.
Starting Page	452
Ending Page	455
File Size	439068
Page Count	4
File Format	PDF
ISBN	9781424450879
DOI	10.1109/NSS.2009.81
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association Rule Mining Partial Decision Tree (PART) Classification algorithms Apriori Association rules Data mining Computational complexity Unsupervised learning DARPA (Defense Advanced Research Projects Agency) Supervised learning Intrusion detection Classification Decision trees Classification tree analysis Testing
Content Type	Text
Resource Type	Article

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