Improving fuzzy c-means clustering based on feature-weight learning

By Xizhao Wang, Yadong Wang, Lijuan Wang. Pattern Recognition Letters, 2004

340 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5344fa76ab9961efb0ee9d45185200ab04485f8a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Mohd Sharfy bin Mohd Rahim
2. Abdolvahab Ehsani Rad
3. Ayman Altameem
4. Mueen Uddin
5. Amjad Rehman
6. Tanzila Saba
7. Alireza Norouzi

Citations in the corpus

• Fuzzy c-means clustering with spatial information for image segmentation (2006, methodology): “For example, the feature-weight learning FCM technique [13] assigns various weights to different features to improve the performance of clustering.”
• Medical Image Segmentation Methods, Algorithms, and Applications (2014, background): “The difference is just the degree of membership for each pint belonging to e ach cluster [82 (cid:1) 84].”