Research Area: Metaheuristic Computing
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.
Keywords:
Affinity propagation
oscillations
priori pairwise constraints
fruit fly optimization
benchmark functions
convergence speed
swarm intelligence algorithms
Author(s) Name: Ruihong Zhou, Qiaoming Liu, Jian Wang, Xuming Han & Limin Wang
Journal name: Neural Computing and Applications
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s00521-020-05431-3
Volume Information: 33, pages 4695–4712
