Shuling Yang
South China Agricultural University
7 Papers
33 Citations
Shuling Yang is an academic researcher from South China Agricultural University. The author has contributed to research in topics: Cluster analysis & Fuzzy clustering. The author has an hindex of 4, co-authored 5 publications. Previous affiliations of Shuling Yang include South China University of Technology.
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Papers
A novel cluster validity index for fuzzy C-means algorithm
Shuling Yang,Kangshun Li,Zhengping Liang,Wei Li,Wei Li,Yu Xue +5 more
- 01 Mar 2018
TL;DR: A new clustering approach with improved morphology similarity distance and the novel cluster validity index is proposed and combined with fuzzy C-means to produce a creative algorithm simply named the OMS-OSC algorithm.
19
A EA- and ACA-based QoS multicast routing algorithm with multiple constraints for ad hoc networks
Wei Li,Wei Li,Kangshun Li,Ying Huang,Shuling Yang,Lei Yang +5 more
- 01 Oct 2017
TL;DR: This paper presents an evolutionary algorithm and an ant colony algorithm to serve as the basis for a QoS multicast routing algorithm (EA-ACA-QMRA), which combines the rapid global search capability and robustness of EAs with the pheromone feedback factors of ACAs while accounting for multiple constraints.
16
Dynamic Fitness Landscape Analysis on Differential Evolution Algorithm
Shuling Yang,Kangshun Li,Wei Li,Wei Li,Weiguang Chen,Yan Chen +5 more
- 28 Oct 2016
TL;DR: This paper focuses on one of evolutionary algorithms named as differential evolution (DE) algorithm, which shows obviously that differential evolution algorithm can calculate low dimension of benchmark functions and is very hard to handle high dimension.
6
An E-commerce Personalized Recommendation Algorithm based on Fuzzy Clustering
Yan Hou,Shuling Yang +1 more
TL;DR: F fuzzy clustering recommendation algorithm is adopted to effectively solve the problem of recommendation quality degradation caused by data sparsity and flexible partition of fuzzy c-means clustering algorithm is used to reduce the dimension of data.
A Clustering Algorithm for Cross-border E-commerce Customer Segmentation
Shuling Yang,Yangyang Hou +1 more
TL;DR: The research shows that the improved K-means clustering algorithm significantly improves the quality of clustering, thus improving the effectiveness and pertinence of cross-border e-commerce marketing activities.