Introducing Partial Matching Approach in Association Rules for Better Treatment of Missing Values

TL;DR

This paper proposes a hybrid partial matching method in association rule mining combined with k-nearest neighbors to improve missing value imputation accuracy, outperforming existing single-approach techniques.

Contribution

It introduces a novel partial matching concept in association rules and combines it with k-nearest neighbors for enhanced missing value imputation accuracy.

Findings

Outperforms previous methods on benchmark datasets

Achieves higher imputation accuracy

Demonstrates effectiveness of hybrid approach

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.