Optimization of unsupervised affinity propagation clustering method

2019 ◽  
Author(s):  
Jihan Alameddine ◽  
Kacem Chehdi ◽  
Claude Cariou
Keyword(s):  
Affinity Propagation ◽  
Clustering Method ◽  
Affinity Propagation Clustering

IDENTIFICATION OF COMMUNITY STRUCTURE IN COMPLEX NETWORKS USING AFFINITY PROPAGATION CLUSTERING METHOD

2008 ◽  
Vol 22 (16) ◽  
pp. 1547-1566 ◽  
Author(s):  
DARONG LAI ◽  
HONGTAO LU
Keyword(s):  
Complex Networks ◽  
High Performance ◽  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Data Sets ◽  
Clustering Method ◽  
Affinity Propagation Clustering ◽  
Community Identification ◽  
Clustering Data ◽  
Log Domain

Identifying communities in complex networks has recently attracted considerable attention in different fields. The goal of community identification is to cluster vertices of a network into groups, which is the same as clustering in machine learning and data mining domains. A recent proposed clustering method called affinity propagation shows high performance in clustering data sets into groups, and it does not require that the number of clusters be pre-specified. In this paper, based on a new method for calculating similarity between pairs of vertices and a transforming method for a given similarity from likelihood to log-domain, we apply that affinity propagation clustering method to identify communities in complex networks. Extensive simulation results demonstrate that affinity propagation clustering algorithm is very effective for identifying community structures in both computer-generated and real-world network data.

scholarly journals An Affinity Propagation Clustering Method Using Hybrid Kernel Function With LLE

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68892-68909 ◽  
Author(s):  
Lin Sun ◽  
Ruonan Liu ◽  
Jiucheng Xu ◽  
Shiguang Zhang ◽  
Yun Tian
Keyword(s):  
Kernel Function ◽  
Affinity Propagation ◽  
Clustering Method ◽  
Affinity Propagation Clustering ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

scholarly journals An Extended Affinity Propagation Clustering Method Based on Different Data Density Types

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
XiuLi Zhao ◽  
WeiXiang Xu
Keyword(s):  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Similar Degree ◽  
Clustering Method ◽  
Data Set ◽  
Affinity Propagation Clustering ◽  
Initial Cluster ◽  
Data Density ◽  
Data Points ◽  
Ap Clustering

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. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data
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