Abstract
Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach. However, one of the greatest disadvantages of this method is its sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Hubers M-estimators and the Yagers Ordered Weighted Averaging (OWA) operators are used in the method proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided.
| Original language | English |
|---|---|
| Pages (from-to) | 49-67 |
| Number of pages | 19 |
| Journal | Pattern Recognition |
| Volume | 58 |
| DOIs | |
| Publication status | Published - 1 Oct 2016 |
Keywords
- Fuzzy c-ordered medoids clustering
- Hubers M-estimators
- Interval-valued data
- Ordered weighted averaging
- Outlier interval data
- Robust clustering
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Fingerprint
Dive into the research topics of 'Fuzzy c-ordered medoids clustering for interval-valued data'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver