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Near-neighbor Propagation Clustering Algorithm Based on Cuckoo Search
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In this paper, a nearest neighbor propagation clustering algorithm (CSB-AP) based on cuckoo search is proposed to solve the problem of poor parameter setting of the AP algorithm. A population-intelligent optimized cuckoo search algorithm is introduced into the AP algorithm to find the appropriate parameters. Two important parameters in the nearest neighbor propagation algorithm are taken as the location of the bird’s nest. The BWP value index is introduced as the bird’s nest fitness in the process of intelligent search, and the minimum value is obtained as the final fitness through the reverse mechanism. The final parameters are substituted into the calculation as the best parameters. The CSB-AP algorithm is verified by the UCI data set, and compared with the traditional AP nearest neighbor propagation clustering algorithm, it is found that the CSB-AP algorithm proposed in this paper is better than the traditional AP nearest neighbor propagation clustering algorithm. By comparison, it can be found that the clustering result obtained by the CSB-AP algorithm is closer to the actual result, and it can be known according to the results of the BWP index, contour coefficient, Recall, and F-measure. The improved algorithm can significantly improve the clustering quality and clustering performance.
Title: Near-neighbor Propagation Clustering Algorithm Based on Cuckoo Search
Description:
In this paper, a nearest neighbor propagation clustering algorithm (CSB-AP) based on cuckoo search is proposed to solve the problem of poor parameter setting of the AP algorithm.
A population-intelligent optimized cuckoo search algorithm is introduced into the AP algorithm to find the appropriate parameters.
Two important parameters in the nearest neighbor propagation algorithm are taken as the location of the bird’s nest.
The BWP value index is introduced as the bird’s nest fitness in the process of intelligent search, and the minimum value is obtained as the final fitness through the reverse mechanism.
The final parameters are substituted into the calculation as the best parameters.
The CSB-AP algorithm is verified by the UCI data set, and compared with the traditional AP nearest neighbor propagation clustering algorithm, it is found that the CSB-AP algorithm proposed in this paper is better than the traditional AP nearest neighbor propagation clustering algorithm.
By comparison, it can be found that the clustering result obtained by the CSB-AP algorithm is closer to the actual result, and it can be known according to the results of the BWP index, contour coefficient, Recall, and F-measure.
The improved algorithm can significantly improve the clustering quality and clustering performance.
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