THE ANALYSIS OF INCOMPLETE DATASET USING FUZZY C-MEDOIDS ALGORITHM WITH A CASE STUDY OF PHYSICAL EXAMINATION DATASET - Chiu, Ming-Chuan; Chen, Yi-Wen (International Journal of Industrial Engineering: Theory, Applications and Practice, 2018)
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.