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Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
R L Cannon1, J V Dave, J C Bezdek
1Department of Computer Science, University of South Carolina, Columbia, SC 29208.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study introduces an Approximate Fuzzy C-Means (AFCM) algorithm that significantly speeds up fuzzy c-means (FCM) clustering. AFCM reduces computation time by using estimates in its equations, making it ideal for image processing tasks.
Area of Science:
- Computer Science
- Data Science
- Image Processing
Background:
- Fuzzy C-Means (FCM) clustering is a widely used data analysis technique.
- Traditional FCM can be computationally intensive, especially for large datasets.
- Optimizing FCM performance is crucial for real-time applications.
Purpose of the Study:
- To propose and numerically compare an Approximate Fuzzy C-Means (AFCM) algorithm against the standard FCM.
- To evaluate the computational efficiency and cluster quality of the AFCM implementation.
- To demonstrate AFCM's applicability in image processing scenarios.
Main Methods:
- Developed an AFCM implementation by approximating exact variates in FCM equations with estimates.
- Utilized lookup tables for computing Euclidean distances and exponentiation within AFCM.
- Performed numerical comparisons on a nine-band digital image dataset.
Main Results:
- AFCM reduced CPU time per iteration to approximately one-sixth of the standard FCM.
- The AFCM implementation maintained the overall quality of terminal clusters.
- Pseudocode for AFCM was provided for practical applications.
Conclusions:
- AFCM offers a significant acceleration of FCM processing.
- The algorithm is particularly effective when the feature space consists of integer-valued coordinates.
- AFCM is a viable alternative for accelerating FCM in image analysis and other relevant fields.
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