Data Imputation Methods for Missing Values in the Context of Clustering
Mehmet S. Aktaş, Sinan Kaplan, Samet Hasan Abacı, Oya Kalıpsız, Utku Görkem Ketenci, Umut Orçun Turgut · Advances in knowledge acquisition, transfer, and management book series/Advances in knowledge acquisition, transfer and management book series · 2018
Missing data is a common problem for data clustering quality. Most real-life datasets have missing data, which in turn has some effect on clustering tasks. This chapter investigates the appropriate data treatment methods for varying missing data scarcity distributions including gamma, Gaussian, and beta distributions. The analyzed data imputation methods include mean, hot-deck, regression, k-nearest neighbor, expectation maximization, and multiple imputation. To reveal the proper methods to deal with missing data, data mining tasks such as clustering is utilized for evaluation. With the experimental studies, this chapter identifies the correlation between missing data imputation methods and missing data distributions for clustering tasks. The results of the experiments indicated that expectation maximization and k-nearest neighbor methods provide best results for varying missing data scarcity distributions.