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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-07-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/155014779807206 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849473326321238016 |
|---|---|
| author | Libin Jiao Rongfang Bie Guangzhi Zhang Shenling Wang Rashid Mehmood |
| author_facet | Libin Jiao Rongfang Bie Guangzhi Zhang Shenling Wang Rashid Mehmood |
| author_sort | Libin Jiao |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-c7fa0ea3dd304f15aaf132afd2f95cea |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c7fa0ea3dd304f15aaf132afd2f95cea2025-08-20T03:24:11ZengWileyInternational 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 |
| 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 |
| title | 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_short | 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 |
| url | https://doi.org/10.1177/155014779807206 |
| work_keys_str_mv | AT libinjiao properglobalsharedpreferencedetectionbasedongoldensectionandgeneticalgorithmforaffinitypropagationclustering AT rongfangbie properglobalsharedpreferencedetectionbasedongoldensectionandgeneticalgorithmforaffinitypropagationclustering AT guangzhizhang properglobalsharedpreferencedetectionbasedongoldensectionandgeneticalgorithmforaffinitypropagationclustering AT shenlingwang properglobalsharedpreferencedetectionbasedongoldensectionandgeneticalgorithmforaffinitypropagationclustering AT rashidmehmood properglobalsharedpreferencedetectionbasedongoldensectionandgeneticalgorithmforaffinitypropagationclustering |