TY - GEN
T1 - A local approach of adaptive affinity propagation clustering for large scale data
AU - Sun, Changyin
AU - Wang, Chenghong
AU - Song, Su
AU - Wang, Yifan
PY - 2009
Y1 - 2009
N2 - Affinity propagation exhibits fast execution speed and finds clusters with low error rate when clustering sparsely related data but its values of parameters are fixed. This paper proposes a modified method named partition adaptive affinity propagation, which can automatically eliminate oscillations and adjust the values of parameters when rerunning affinity propagation procedure to yield optimal clustering results, with high execution speed and precision. Experiments are carried on UCI datasets and CaltechlOl dataset, and ORL faces dataset. The results verify that this adaptive method is effective and feasible.
AB - Affinity propagation exhibits fast execution speed and finds clusters with low error rate when clustering sparsely related data but its values of parameters are fixed. This paper proposes a modified method named partition adaptive affinity propagation, which can automatically eliminate oscillations and adjust the values of parameters when rerunning affinity propagation procedure to yield optimal clustering results, with high execution speed and precision. Experiments are carried on UCI datasets and CaltechlOl dataset, and ORL faces dataset. The results verify that this adaptive method is effective and feasible.
UR - https://www.scopus.com/pages/publications/70449440349
U2 - 10.1109/IJCNN.2009.5178601
DO - 10.1109/IJCNN.2009.5178601
M3 - 会议稿件
AN - SCOPUS:70449440349
SN - 9781424435531
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2998
EP - 3002
BT - 2009 International Joint Conference on Neural Networks, IJCNN 2009
T2 - 2009 International Joint Conference on Neural Networks, IJCNN 2009
Y2 - 14 June 2009 through 19 June 2009
ER -