Comparative Analysis of Incomplete Business Data Clustering

Abstract

Incomplete values can significantly reduce the accuracy and usability of missing data. In particular, in analyzing commercial data sets, missing values often lead to the dilemma of data selection. It means that a common way to deal with missing data is to delete the sample that contains the missing attribute. However, this can lead to biased and invalidated conclusions, as some data are too critical to be omitted. Therefore, we should use some method to fill the data set rather than delete the data with missing values. The filling of missing data is divided into supervised learning and unsupervised learning. This paper compares six benchmark business datasets by adopting several different data imputation methods and supplementing the missing data with a clustering approach (unsupervised learning). The results are guided to dealing with incomplete business data.