An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data

Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algo-rithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propaga-tion and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strat-egy to enhance local search and convergence efficiency. The benchmark function experi-ments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This article introduces an improved affinity propagation algorithm based on optimization of preference (APBOP) for automatic clustering on high-dimensional data. APBOP aims to address the challenges of feature extraction from high-dimensional data and the sensitivity of the clustering performance to preference. The proposed method utilizes dimensionality reduction and preference optimization techniques to improve the effectiveness of affinity propagation.