A Novel Approach for Imputation of Missing Value Analysis using Canopy K-means Clustering

Abstract

Multiple imputation provides a useful strategy for dealing with data sets with missing value. Instead of filling in a single value for each missing value. Imputation is a term that denotes a procedure that replaces the missing values in a data set by some possible values. In this work missing values are being inserted completely at random (MCAR). Dataset taken for this work is heart disease dataset that contains some missing values. The main problem of this k means algorithm to don’t take the random position of the data point, it’s one of the k means for only two way cluster. These multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. In the proposed method is used for the canopy k-means clustering algorithm using with the predication of higher accuracy calculate the particular data sets.

Keywords: missing value, multiple imputation, k-means algorithm and canopy clustering.