Computer Science ›› 2017, Vol. 44 ›› Issue (10): 187-192.doi: 10.11896/j.issn.1002-137X.2017.10.035

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Affinity Propagation Clustering Algorithm Based on Density Adjustment and Manifold Distance

XIA Chun-meng, NI Zhi-wei, NI Li-ping and ZHANG Lin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: As affinity propagation(AP)clustering is sensitive to the dataset with scaling parameter and various form while calculating the similarity matrix and the cluster result is not ideal,an affinity propagation clustering algorithm based on density adjustment and manifold distance was proposed.The algorithm introduces local density of data and manifold theory into affinity propagation clustering,and uses a way of distance measure based on manifold structure and density adjustment to describe the clusters’ actual structure better,making up the similarity matrix’s deficiency.At the same time,the algorithm is more efficient.Simulation experiment was done on artificial datasets and standard datasets.The result shows the effectiveness and superiority of proposed algorithm.

Key words: Affinity propagation clustering,Density adjustment,Manifold similarity,Multi-scale dataset,Various form dataset

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