Research Article
Classification of Microarray Data Using Kernel Fuzzy Inference System
Algorithm 1
Kernel subtractive clustering.
| Input: The dataset , radius . | | Output: Optimal number of clusters, their centroid and sigma (). | | Compute the potential for each data point using (6). | | Choose the data point whose potential value is highest as a cluster centroid. | | Discard and recompute the potential value for each using (7). | | If then | | Accept as a cluster center and continue. | | else if then | | Reject and end the clustering process. | | else | | = shortest of the distance between and all previously found cluster centers. | | if ( then | | Accept as a cluster center and continue. | | else | | Reject and set the potential at to 0. Select the data point with the next highest potential as the new and reset. | | end if | | end if | | |
|
