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Domain Knowledge Blended Affinity Propagation |
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PP: 717-723 |
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Author(s) |
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Wei Chen,
Qichong Tian,
Xiaorong Jiang,
Zhibo Tang,
Caihua Guo,
Xinzheng Xu,
Hong Zhu,
Shifei Ding,
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Abstract |
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As an important clustering algorithm, Affinity Propagation (AP) algorithm can quickly find the reasonable clustering center.
But the AP algorithm is difficult to make correct clustering, when the sample in the weaker separability feature space. In this paper, the
Domain Knowledge Blended Affinity Propagation (DKB-AP) algorithm is proposed. Combining the domain knowledge function and
the similarity measure of the AP algorithm, the algorithm makes iterating to obtain the clustering result. The experimental data are three
random sample sets, including two sample sets whose subclass aggregation degree are good, one sample set whose subclass aggregation
degree is weak. The clustering results, Fowlkes-Mallows Validity Index and Error Ratefor for the Sets are analysed. The results show
that the clustering result in the weaker separability feature space by DKB-AP algorithm is almost consistent with the clustering result
in the separability feature space by AP algorithm. |
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