Abstract
Unlike traditional clustering techniques like K-means, K-nearest neighborhood where the number of initial clusters is known apriori, in affinity propagation (AP) based approach the numbers of clusters present in a given dataset are chosen automatically. The initial cluster centers in AP are termed as exemplars which are initialized randomly, and their numbers are automatically set and refined with the progress in the iteration. The AP has improved accuracy than K-means and has much lower computational time than the evolutionary based automatic clustering approaches and density based clustering like DBSCAN. The original AP algorithm was based on Euclidean distance. Here a Correlation based affinity propagation (CAP) algorithm is introduced considering Pearson Correlation as affinity measure. Simulation results reveal that the CAP provides effective clusters than that achieved by AP for datasets having a large number of attributes. The proposed algorithm is applied to categorize aggressive and regular actions of 3D human models. Extensive simulation studies on fifteen cases reveal the superior performance of CAP on finding the exact number of clusters as well as numbers of points in each cluster close to that of the true partition.
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References
Frey, B.J. and Dueck, D.: Clustering by passing messages between data points, Science Express, 315, 5814, 972–976 (2007).
Ren, T., Zeng, W., Wang, N., Chen, L. and Wang, C.: A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering. Magnetic resonance imaging, 32, 6, 736–746 (2014).
Xia, H., Chen, X. and Guo, P.: A shadow detection method for remote sensing images using affinity propagation algorithm. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 3116–3121 (2009).
Kokawa, Y., Wu, H. and Chen, Q.: Improved affinity propagation for gesture recognition. Procedia Computer Science, In: 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, 22, pp. 983–990 (2013).
Ying, Z. and Changgang, J.: A kind of routing algorithm for heterogeneous wireless sensor networks based on affinity propagation. In: 26th Chinese Control and Decision Conference, pp. 2481–2485 (2014).
Lee, S.H. and Sohn, I.: Affinity propagation for energy-efficient BS operations in green cellular networks. IEEE Trans. on Wireless Communications, 14, 8, 4534–4545 (2015).
Hassanabadi, B., Shea, C., Zhang, L. and Valaee, S.: Clustering in vehicular ad hoc networks using affinity propagation. Ad Hoc Networks, 13, 535–548 (2014).
Liu, D. and Jiang, M.: Affinity propagation clustering on oral conversation texts. In: IEEE 11th International Conference on Signal Processing, 3, pp. 2279–2282 (2012).
Wang, C.D., Lai, J.H., Suen, C.Y. and Zhu, J.Y.: Multiexemplar affinity propagation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 35, 9, 2223–2237 (2013).
Gan, G. and Ng, M.K.P.: Subspace clustering using affinity propagation. Pattern Recognition, 48, 4, 1455–1464 (2015).
Hang, W., Chung, F.L. and Wang, S.: Transfer affinity propagation-based clustering. Information Sciences, 348, 337–356 (2016).
Dueck D.: Affinity propagation: clustering data by passing messages, Doctoral Thesis. University of Toronto, (2009).
Theodoridis T. et al.: Ubiquitous robotics in physical human action recognition: a comparision between dynamic ANNS and GP, Proc. of IEEE international conference on robotics and automation, pp. 3064–3069 (2008).
Nanda, S. J. and Panda G.: Automatic clustering algorithm based on multi objective immunised PSO to classify actions of 3D human models, Engineering Application of Artificial Intelligence, 26, 1429–1441 (2013).
Nanda, S. J. and Panda G.: Design of computationally efficient density-based algorithms, Data & Knowledge Engineering, 95, 25–38 (2015).
UCI Repository of Machine Learning: https://archive.ics.uci.edu/ml/datasets.html.
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Gouda, B., Nanda, S.J. (2018). Classifying Aggressive Actions of 3D Human Models Using Correlation Based Affinity Propagation Algorithm. In: Bhateja, V., Tavares, J., Rani, B., Prasad, V., Raju, K. (eds) Proceedings of the Second International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-8228-3_18
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DOI: https://doi.org/10.1007/978-981-10-8228-3_18
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