An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points....
Main Authors: | XiuLi Zhao, WeiXiang Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2015-01-01
|
Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2015/828057 |
Similar Items
-
Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking
by: Tao Zhang, et al.
Published: (2015-09-01) -
Fast Clustering by Affinity Propagation Based on Density Peaks
by: Yang Li, et al.
Published: (2020-01-01) -
Affinity Propagation: Clustering Data by Passing Messages
by: Dueck, Delbert
Published: (2009) -
Affinity Propagation: Clustering Data by Passing Messages
by: Dueck, Delbert
Published: (2009) -
A New Measurement Method to Calculate Similarity of Moving Object Spatio-Temporal Trajectories by Compact Representation
by: Xu WeiXiang, et al.
Published: (2011-12-01)