Paper Info
Title
Introducing Partial Matching Approach in Association Rules for Better Treatment of Missing Values
Abstract
Handling missing values in training datasets for constructing learning models or extracting useful information is considered to be an important research task in data mining and knowledge discovery in databases. In recent years, lot of techniques are proposed for imputing missing values by considering attribute relationships with missing value observation and other observations of training dataset. The main deficiency of such techniques is that, they depend upon single approach and do not combine multiple approaches, that why they are less accurate. To improve the accuracy of missing values imputation, in this paper we introduce a novel partial matching concept in association rules mining, which shows better results as compared to full matching concept that we described in our previous work. Our imputation technique combines the partial matching concept in association rules with k-nearest neighbor approach. Since this is a hybrid technique, therefore its accuracy is much better than as compared to those techniques which depend upon single approach. To check the efficiency of our technique, we also provide detail experimental results on number of benchmark datasets which show better results as compared to previous approaches.
Year
Venue
Keywords
2009
Clinical Orthopaedics and Related Research
artificial intelligent,data mining,data structure,k nearest neighbor,missing values,association rule,association rule mining
Field
DocType
Volume
Data mining,Computer science,Association rule learning,Artificial intelligence,Knowledge extraction,Learning models,Imputation (statistics),Missing data,Machine learning
Journal
abs/0904.3
Citations 
PageRank 
References 
1
0.35
1
Authors
5
Name
Order
Citations
PageRank
Shariq Bashir116713.48
Saad Razzaq2153.96
Umer Maqbool361.08
Sonya Tahir461.08
Abdul Rauf Baig512615.82