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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