THE ANALYSIS OF INCOMPLETE DATASET USING FUZZY C-MEDOIDS ALGORITHM WITH A CASE STUDY OF PHYSICAL EXAMINATION DATASET

Authors

  • Ming-Chuan Chiu National Tsing Hua University, Taiwan
  • Yi-Wen Chen National Tsing Hua University, Taiwan

DOI:

https://doi.org/10.23055/ijietap.2018.25.1.3759

Keywords:

fuzzy c-medoids clustering, incomplete dataset, partial distance strategy, physical examination

Abstract

Clustering is an unsupervised approach for unlabeled data classification with less prior information. Despite the usefulness of data clustering, few of clustering algorithms are applicable to deal with incomplete datasets, which are datasets with missing values. In addition, this type of datasets takes the majority in real-world applications. Therefore, a new algorithm combining fuzzy c-medoids clustering and partial distance strategy is developed to overcome the problem. A physical examination case study demonstrates the advantage of the proposed algorithm. Our results demonstrate the outperformance of data clustering to reveal potential patients than the traditional dichotomy identification and can be employed to preventative medicine. 

Author Biography

Ming-Chuan Chiu, National Tsing Hua University, Taiwan

Ming-Chuan Chiu is an associate professor in the Department of Industrial Engineering and Engineering Management at National Tsing-Hua University in Taiwan where he obtained his BS and MS degrees. IE of NTHU is rank #1 IE department in Taiwan. He received his PhD degree in the Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests focus on design for X factors, supply chain and product design integration, and service innovation. The aim of the above interests is to solve problems in product, service, and system development stage using systems thinking. 

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Published

2018-02-28

How to Cite

Chiu, M.-C., & Chen, Y.-W. (2018). THE ANALYSIS OF INCOMPLETE DATASET USING FUZZY C-MEDOIDS ALGORITHM WITH A CASE STUDY OF PHYSICAL EXAMINATION DATASET. International Journal of Industrial Engineering: Theory, Applications and Practice, 25(1). https://doi.org/10.23055/ijietap.2018.25.1.3759

Issue

Vol. 25 No. 1 (2018): Including ISMI 2016 Special Issue

Section

Special Issue: ISMI 2016

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