Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation Clustering
Affinity propagation (AP) clustering is a well-known effective clustering algorithm that outperforms other traditional clustering algorithms. However, the quality of clustering results depends considerably on related sensitive parameters (i.e., preferences and the damping factor). Thus, a feasible p...
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Online Access: | https://doi.org/10.1177/155014779807206 |
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doaj-c7fa0ea3dd304f15aaf132afd2f95cea2020-11-25T03:34:05ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772016-07-011210.1177/155014779807206Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation ClusteringLibin Jiao0Rongfang Bie1Guangzhi Zhang2Shenling Wang3Rashid Mehmood4 College of Information Science and Technology, Beijing Normal University, Beijing 100875, China College of Information Science and Technology, Beijing Normal University, Beijing 100875, China College of Information Science and Technology, Beijing Normal University, Beijing 100875, China College of Information Science and Technology, Beijing Normal University, Beijing 100875, China Department of Computer Science and Information Technology, University of Management Sciences and Information Technology, Kotli, Azad Jammu and Kashmir 11100, PakistanAffinity propagation (AP) clustering is a well-known effective clustering algorithm that outperforms other traditional clustering algorithms. However, the quality of clustering results depends considerably on related sensitive parameters (i.e., preferences and the damping factor). Thus, a feasible procedure based on golden section (GS) and the genetic algorithm (GA) is proposed. This procedure, called the “GS/GA-AP” algorithm, can perform proper global shared preference detection, including identifying a suitable number of clusters. A global shared preference is provided using the GS value between the minimum and maximum of similarities for AP as a default option, and the unsatisfactory clustering result becomes robust when the parameter with GA is selected. Finally, satisfactory experiments using one simulation data set and eight benchmark data sets are performed to verify the effectiveness of the proposed algorithm. The results indicate that GS/GA-AP clearly outperforms the original AP clustering algorithm.https://doi.org/10.1177/155014779807206 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Libin Jiao Rongfang Bie Guangzhi Zhang Shenling Wang Rashid Mehmood |
spellingShingle |
Libin Jiao Rongfang Bie Guangzhi Zhang Shenling Wang Rashid Mehmood Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation Clustering International Journal of Distributed Sensor Networks |
author_facet |
Libin Jiao Rongfang Bie Guangzhi Zhang Shenling Wang Rashid Mehmood |
author_sort |
Libin Jiao |
title |
Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation Clustering |
title_short |
Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation Clustering |
title_full |
Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation Clustering |
title_fullStr |
Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation Clustering |
title_full_unstemmed |
Proper Global Shared Preference Detection Based on Golden Section and Genetic Algorithm for Affinity Propagation Clustering |
title_sort |
proper global shared preference detection based on golden section and genetic algorithm for affinity propagation clustering |
publisher |
SAGE Publishing |
series |
International Journal of Distributed Sensor Networks |
issn |
1550-1477 |
publishDate |
2016-07-01 |
description |
Affinity propagation (AP) clustering is a well-known effective clustering algorithm that outperforms other traditional clustering algorithms. However, the quality of clustering results depends considerably on related sensitive parameters (i.e., preferences and the damping factor). Thus, a feasible procedure based on golden section (GS) and the genetic algorithm (GA) is proposed. This procedure, called the “GS/GA-AP” algorithm, can perform proper global shared preference detection, including identifying a suitable number of clusters. A global shared preference is provided using the GS value between the minimum and maximum of similarities for AP as a default option, and the unsatisfactory clustering result becomes robust when the parameter with GA is selected. Finally, satisfactory experiments using one simulation data set and eight benchmark data sets are performed to verify the effectiveness of the proposed algorithm. The results indicate that GS/GA-AP clearly outperforms the original AP clustering algorithm. |
url |
https://doi.org/10.1177/155014779807206 |
work_keys_str_mv |
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