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

  • Duy-Tai Dinh
  • Van-Nam Huynh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)

Abstract

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.

Keywords

Data mining Partitional clustering Categorical data Missing values Incomplete dataset Decision tree-based imputation 

Notes

Acknowledgment

This paper is based upon work supported in part by the Air Force Office of Scientific Research/Asian Office of Aerospace Research and Development (AFOSR/AOARD) under award number FA2386-17-1-4046.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Knowledge ScienceJapan Advanced Institute of Science and TechnologyNomiJapan

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