NDLI: Missing nominal data imputation using association rule based on weighted voting method

Content Provider	IEEE Xplore Digital Library
Author	Jianhua Wu Qinbao Song Junyi Shen
Copyright Year	2008
Description	Author affiliation: Sch. Of Electron. & Info. Eng., Xi'an Jiaotong Univ., Xi'an (Jianhua Wu; Qinbao Song; Junyi Shen)
Abstract	With the rapid increase in the use of databases, missing data make up an important and unavoidable problem in data management and analysis. Because the mining of association rules can effectively establish the relationship among items in databases, therefore, discovered rules can be applied to predict the missing data. In this paper, we present a new method that uses association rules based on weighted voting to impute missing data. Three databases were used to demonstrate the performance of the proposed method. Experimental results prove that our method is feasible in some databases. Moreover, the proposed method was evaluated using five classification problems with two incomplete databases. Experimental results indicate that the accuracy of classification is increased when the proposed method is applied for missing attribute values imputation.
Starting Page	1157
Ending Page	1162
File Size	291824
Page Count	6
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4633945
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Software Artificial neural networks Joints Software algorithms Association rules Algorithm design and analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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