Applying Data Clustering Feature to Speed Up Ant Colony Optimization

Algorithm 2

Input parameters:

𝑇₀: Training Set

𝑚₀: The initial number of classes.

𝜀: The stop threshold for clustering.

Output:

Num: The final number of classes.

CLS: The partition of   𝑇₀, in which each class is com-pact.

SLC Algorithm:

Step  1. Initialization: Let  𝑇=𝑇₀,  𝑚=𝑚₀,  CLS=𝜙, and 𝑝=0.

Step  2. For (𝑖=0;𝑖<[log₂𝑚];𝑖++) /*Note: [log₂𝑚] denotes the integer */

{  Step  2.1. Generate initial centroids set  𝐶⁰={𝑐_𝑖⁽⁰⁾∣1≤𝑖≤𝑚}.

 Step  2.2. Call Subroutine1 (𝑇,𝑚,𝐶⁽⁰⁾,𝜀,𝑝,𝜑(𝑇),𝜎(𝑇))

 Step  2.3.  CLS=CLS∪𝜑(𝑇);

 Step  2.4. 𝑇=𝜎(𝑇);

 /* Note: Increase 𝑝 to get smaller compact class */

 Step  2.5.  𝑚𝑚=2;  𝑝=𝑝+1

}

Step  3. Every residual point 𝑥 in the last set  𝜎(𝑇)  is regarded as a class  {𝑥}. And let  CLS=CLS∪{𝑥}.

Let  Num  denote the number of classes contained in  CLS. The two outputs are  CLS  and  Num.