NDLI: MrCAR: A Multi-relational Classification Algorithm Based on Association Rules

Content Provider	IEEE Xplore Digital Library
Author	Yingqin Gu Hongyan Liu Jun He Bo Hu Xiaoyong Du
Copyright Year	2009
Abstract	Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules is one of the most effective classification method, whose accuracy is higher and discovered rules are easier to understand comparing with classical classification methods. However, current algorithms for classification based on association rules is single table oriented, which means they can only apply to the data stored in a single relational table. Directly applying these algorithms in multi-relational data environment will result in many problems. This paper proposes a novel algorithm MrCAR for classification based on association rules in multi-relational data environment. MrCAR mines relevant features in each table to predict the class label. Close item sets technique and Tuple ID Propagation method are used to improve the performance of the algorithm. Experimental results show that MrCAR has higher accuracy and better understandability comparing with a typical existing multi relational classification algorithm.
Starting Page	256
Ending Page	260
File Size	261001
Page Count	5
File Format	PDF
ISBN	9780769538174
DOI	10.1109/WISM.2009.60
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Associative classification Machine learning algorithms Itemsets Relational databases Machine learning Multi-relational classification Data engineering Classification algorithms Association rules Data mining Information systems
Content Type	Text
Resource Type	Article

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