Fuzzy Data Clustering Using a Self-Organization Procedure
碩士 === 國立新竹教育大學 === 人資處數學教育碩士班 === 99 === Several cluster methods were used for analysis of LR-type Fuzzy data. However, those measures suffered different levels of drawbacks. For overcoming the existing problems in those clustering methods, in this paper, we proposed a new clustering method based o...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2011
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/61626230723542069054 |
| Summary: | 碩士 === 國立新竹教育大學 === 人資處數學教育碩士班 === 99 === Several cluster methods were used for analysis of LR-type Fuzzy data. However, those measures suffered different levels of drawbacks. For overcoming the existing problems in those clustering methods, in this paper, we proposed a new clustering method based on a self-organization procedure for handling LR-type fuzzy numbers. When the proposed clustering algorithm was employed, the LR-type data could be self-organized by using their distance of similarity and resulted in a good clustering classification. In addition, the proposed clustering method could find out the outlier from the Fuzzy data set. For examining the effectiveness of the proposed approach, we then apply this algorithm to two real data sets which are students’ learning performance and patients’ blood pressure data. The results indicated that the proposed method did obtain good clustering results for these real data sets.
|
|---|