Comparison of Linguistic and Regular Hard C-Means in Postoperative Patient Data
Sansanee Auephanwiriyakul*, and Nipon Theera-Umpon**
*Computer Engineering Department, Chiang Mai University, Chiang Mai 50200, Thailand
**Electrical Engineering Department, Chiang Mai University, Chiang Mai 50200, Thailand
Classification is one of the main topics in pattern recognition. Generally, classification problems deal with numeric data. However, there are uncertainties in the data frequently, such as data collected in management questionnaires, postoperative patient data, etc. Hence, classification problems on linguistic data are challenging. In this paper, we compare the performance of the linguistic Hard C-Means (LHCM) with the regular Hard C-Means (HCM) through the postoperative patient data. We found that the LHCM performs better than the regular HCM in terms of the classification rate. In addition, we are able to interpret each cluster attribute by the input linguistic label.