Computer Science ›› 2017, Vol. 44 ›› Issue (2): 98-102, 106.doi: 10.11896/j.issn.1002-137X.2017.02.013

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Missing Data Imputation Approach Based on Tuple Similarity

WANG Jun-lu, WANG Ling, WANG Yan and SONG Bao-yan   

  • Online:2018-11-13 Published:2018-11-13

Abstract: With the development of Internet and information technology,the data loss,damage and other problems become more and more popular.Especially with data collection from the manual to machine,storage medium is not stability,transmission omissions appear and other reasons,resulting that missing data are more serious.A large number of missing values in the database not only seriously affect the quality of the query,but also affect the accuracy of the results of data mining and data analysis.At present,there is not a general method to deal with missing data.Most of the strategies are based on the problem of the missing value of a certain type.Therefore,in view of this complex situation of that the different deletion types also appear in the incomplete data at the same time,this paper put forward missing data imputation approach based on tuple similarity(IATS).Incomplete data sets of weighted association rules are extracted by the method of data mining,and according to the rules imputate normal missing data,and for abnormal missing data,this paper introduced data recommendation algorithm,the recommended screening strategy of tuple similarity calculation and the realization of the corresponding fill,and then it greatly improves the data effective utilization rate and user query result quality.The experimental results show that the IATS strategy has better accuracy under the premise of ensuring the filling ratio.

Key words: Massive data,Deletion type,Weighted association rules,Tuple similarity

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