Indian Journal of Science and Technology

Abstract

Objectives: Exploratory data study is regularly indispensable to evaluate a potential premise for the subsequent studies such as grouping the data in clusters or diversifying the data in classification. Very common incident in the real data is incompleteness. Methods/Statistical Analysis: This problem can result in the biased treatment comparisons and also impacts the overall statistical power of the study. Missing data are proposed in several methods. The central idea of this proposed method is to handle the uncertainty of the missing values due to the vagueness arises in the real world datasets. This research work overcomes the inconsistency of the missing datasets and the proposed method tolerates the missing values using the fuzzy based K-NN. Three different well known datasets are used in this paper. Findings: The results demonstrate that the proposed method is capable of imputing the missing values even with high presence of missing values and overwhelmed the problem of uncertainty precisely. Application/Improvements: As compared to other techniques the proposed fuzzy based K-NN gives high inconsistency. In future it can be improved in concentrating with big dataset and more effectively and efficiently result could be substituted by applying the expected value.
Keywords: Data Mining, Fuzzy, Imputation, K-NN, Missing Values, Uncertainty