Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization

Affinity propagation (AP) is a clustering method that takes as input measures of similarity between pairs of data points. As the oscillations and preference value need to be preset, the algorithm precision could not be controlled exactly. To improve the performance of AP, this study utilizes priori...

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Bibliographic Details
Published in:Neural computing & applications 2021-05, Vol.33 (10), p.4695-4712
Main Authors: Zhou, Ruihong, Liu, Qiaoming, Wang, Jian, Han, Xuming, Wang, Limin
Format: Article
Language:English
Subjects:
Affinity
Algorithms
Artificial Intelligence
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Data points
Datasets
Density
Image Processing and Computer Vision
Optimization
Probability and Statistics in Computer Science
Propagation
S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
Similarity
Special Issue on Higher Level Artificial Neural Network Based Intelligent Systems
Swarm intelligence
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Summary:Affinity propagation (AP) is a clustering method that takes as input measures of similarity between pairs of data points. As the oscillations and preference value need to be preset, the algorithm precision could not be controlled exactly. To improve the performance of AP, this study utilizes priori pairwise constraints to obtain the reliable similarity matrix named semi-supervised affinity propagation (SAP). To find the best solution in domain of preference value, this study also proposes an improved fruit fly optimization (IFO) to optimize the unknown parameters of the SAP model. The IFO algorithm has introduced the fuzzy density mechanism to enhance the searching capacities of fruit fly individuals. The benchmark functions experiments indicate that the IFO algorithm has better precision and convergence speed than other compared swarm intelligence algorithms. We used SAP that based on IFO to identify UCI datasets and synthetic datasets. The simulation results show that proposed clustering algorithm produces significantly better clustering quality and accuracy results. In addition, we utilized the improved model to analyse the seismic data. The clustering results indicated that the proposed model had the better research potential and the good application value.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05431-3