Mathematical Problems in Engineering / 2021 / Article / Alg 1

Research Article

Boosted Fuzzy Granular Regression Trees

Algorithm 1

Clustering algorithm with automatic optimization of cluster centers.
 Input: instance set , maximum iteration , threshold value
 Output: optimization cluster center set
(1) Remove instances missing some attribute values.
(2) Normalize each attribute value into .
(3)Let evaluated set be an empty set.
(4)Initialize current iteration.
(5) WHILE OR
(6)  . Initialize the current cluster center set.
(7)  . Initialize the number of cluster center.
(8)  Random choose 1 instance point as cluster center.
(9)  
(10)  
(11)  WHILE
(12)   
(13)   The probability of that is selected as next cluster center
(14)   p = GenProb(); Random generate a probability.
(15)   IF THEN
(16)  END WHILE
(17). Calculate the loss function value and cluster center in this iteration and update the evaluated set.
(18)Update the current iteration.
(19) END WHILE
(20). In the evaluated set , choose the cluster center set with minimum loss function value.
  Return the optimization cluster centers and their number.
(21), ( represents the number of elements of the set).

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