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.
(6)  . Initialize the current cluster center set.
(7)  . Initialize the number of cluster center.
(8)  Random choose 1 instance point as cluster center.
(11)  WHILE
(13)   The probability of that is selected as next cluster center
(14)   p = GenProb(); Random generate a probability.
(15)   IF THEN
(17). Calculate the loss function value and cluster center in this iteration and update the evaluated set.
(18)Update the current iteration.
(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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