Research Article
Applying Data Clustering Feature to Speed Up Ant Colony Optimization
Input parameters: | : Training Set | : The initial number of classes. | : The stop threshold for clustering. | Output: | Num: The final number of classes. | CLS: The partition of , in which each class is com-pact. | SLC Algorithm: | Step 1. Initialization: Let , , , and . | Step 2. For () /*Note: denotes the integer */ | Step 2.1. Generate initial centroids set . | Step 2.2. Call Subroutine1 | Step 2.3. ; | Step 2.4. ; | /* Note: Increase to get smaller compact class */ | Step 2.5. ; | } | Step 3. Every residual point in the last set is regarded as a class . And let . | Let Num denote the number of classes contained in CLS. The two outputs are CLS and Num. |
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