Journal
NEURAL COMPUTING & APPLICATIONS
Volume 33, Issue 10, Pages 4695-4712Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-020-05431-3
Keywords
Semi-supervised; Affinity propagation; Fruit fly optimization alogorithm; Fuzzy density; Seismic data analysis
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Funding
- National Science Foundation of China [61472049, 61572225, 61202309]
- key scientific research projects of colleges and universities of Henan Province [21A520012]
- Jilin province social science fund project [2019B69]
- 2018 Jilin province higher education teaching reform research project
- 2018 Jilin university of finance and economics key project
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The study introduces Semi-Supervised Affinity Propagation (SAP) and Improved Fruit Fly Optimization (IFO) algorithm to optimize clustering models, with experimental results showing that IFO algorithm has better precision and convergence speed. Through experiments on seismic data and other datasets, the proposed model demonstrates better research potential and application value.
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.
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