Affinity Propagation Clustering Based on Variable-Similarity Measure
| Autor: | Wang Suo-ping, Dong Jun, Xiong Fan-lun |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Data processing
Computer science business.industry Data cluster ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Frame (networking) Pattern recognition Similarity measure Measure (mathematics) Physics::Fluid Dynamics Variable (computer science) Physics::Accelerator Physics Affinity propagation Artificial intelligence Electrical and Electronic Engineering Cluster analysis business |
| Zdroj: | JOURNAL OF ELECTRONICS INFORMATION & TECHNOLOGY. 32:509-514 |
| ISSN: | 1009-5896 |
| DOI: | 10.3724/sp.j.1146.2009.01066 |
| Popis: | Affinity Propagation (AP) clustering is not fit to deal with multi-scale data cluster as well as the arbitrary shape cluster issue. Therefore, an improved affinity propagation clustering algorithm AP-VSM (Affinity Propagation based on Variable-Similarity Measure) is proposed embarking from the token of data distribution characters. First, a kind of variable-similarity measure method is devised according of characters of global and local data distribution, which has the ability of describing the characters of data clustering effectively. Then AP-VSM clustering algorithm is proposed base on the frame of traditional AP algorithm, and this method has extended data processing capacity compared with traditional AP. The simulation results show that the new method is outperforming traditional AP algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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