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A comparison between rule based and association rule mining algorithms

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conference contribution
posted on 06.12.2017, 00:00 by Mohammed MazidMohammed Mazid, Kevin TickleKevin Tickle
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 ofassociation 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.

History

Start Page

452

End Page

455

Number of Pages

4

Start Date

01/01/2009

ISBN-13

9780769538389

Location

Gold Coast, Queensland, Australia

Publisher

IEEE

Place of Publication

USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Name of Conference

International Conference on Network and System Security