Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization
- PDF / 1,955,138 Bytes
- 18 Pages / 595.276 x 790.866 pts Page_size
- 24 Downloads / 177 Views
(0123456789().,-volV)(0123456789(). ,- volV)
S.I. : HIGHER LEVEL ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT SYSTEMS
Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization Ruihong Zhou1
•
Qiaoming Liu2
•
Jian Wang3 • Xuming Han4 • Limin Wang1
Received: 27 July 2020 / Accepted: 7 October 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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 semisupervised 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 Semi-supervised Affinity propagation Fruit fly optimization alogorithm Fuzzy density Seismic data analysis
1 Introduction Clustering is an unsupervised classification method in the field of machine learning [1, 2]. It is aimed to separate elements into different categories based on internal
Ruihong Zhou and Qiaoming Liu have contributed equally to this work. & Qiaoming Liu [email protected] 1
School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China
2
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
3
College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, China
4
School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
common characteristics [3]. Additionally, there have been a large number of clustering methods applied in different fields, such as web mining [4, 5], pattern recognition [6, 7], image segmentation [8], spatial database analysis [9], document retrieval [10, 11]and gene identification [12–14]. Detailedly, Wu et al. [4] proposed hybrid Web service tag recommendation strategy, named WSTRec, which employs tag co-occurrence and semantic relevance measurement for tag recommendation. Gong et al. [6] presented an improved fuzzy C-means algo
Data Loading...
