k-CCM: A Center-Based Algorithm for Clustering Categorical Data with Missing Values

This paper focuses on solving the problem of clustering for categorical data with missing values. Specifically, we design a new framework that can impute missing values and assign objects into appropriate clusters. For the imputation step, we use a decision tree-based method to fill in missing values. For the clustering step, we use a kernel density estimation approach to define cluster centers and an information theoretic-based dissimilarity measure to quantify the differences between objects. Then, we propose a center-based algorithm for clustering categorical data with missing values, namely k-CCM. An experimental evaluation was performed on real-life datasets with missing values to compare the performance of the proposed algorithm with other popular clustering algorithms in terms of clustering quality. Generally, the experimental result shows that the proposed algorithm has a comparative performance when compared to other algorithms for all datasets.