Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Maximum Consistency of Incomplete Datavia Non-Invasive Imputation
Artificial Intelligence Review
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Using association rules to make rule-based classifiers robust
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Ramp: high performance frequent itemset mining with efficient bit-vector projection technique
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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The quality of training data for knowledge discovery in databases (KDD) and data mining depends upon many factors, but handling missing values is considered to be a crucial factor in overall data quality. Today real world datasets contains missing values due to human, operational error, hardware malfunctioning and many other factors. The quality of knowledge extracted, learning and decision problems depend directly upon the quality of training data. By considering the importance of handling missing values in KDD and data mining tasks, in this paper we propose a novel Hybrid Missing values Imputation Technique (HMiT) using association rules mining and hybrid combination of k-nearest neighbor approach. To check the effectiveness of our HMiT missing values imputation technique, we also perform detail experimental results on real world datasets. Our results suggest that the HMiT technique is not only better in term of accuracy but it also take less processing time as compared to current best missing values imputation technique based on k-nearest neighbor approach, which shows the effectiveness of our missing values imputation technique.