Abstract
Dealing with dynamic data stream has become one of the most active research fields for Internet of Things (IoT). Specifically, clustering toward dynamic data stream is a necessary foundation for numerous IoT platforms. In this paper, we focus on dynamic exemplar-based clustering models. In terms of the maximum a priori principle, under the probability framework, we first summarize a unified explanation for two typical exemplar-based clustering models, namely enhanced \(\alpha\)-expansion move (EEM) and affinity propagation (AP). Then, a new dynamic exemplar-based data stream clustering algorithm called DSC is proposed accordingly. The distinctive merit of the proposed algorithm DSC is that we can simply utilize the framework of EEM algorithm through modifying the definitions of several variables and do not need to design another optimization mechanism. Moreover, algorithm DSC is capable of dealing to two cases of similarities. In contrast to both AP and EEM, our experimental results indicate the power of algorithm DSC for real-world IoT data streams.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ashton K (2009) That internet of things thing. RFID J 22(7):97–114
Ostermaier B, Römer K, Mattern F, Fahrmair M, Kellerer W (2010) A real-time search engine for the web of things. In: Internet of Things, pp 1–8. IEEE, Piscataway
Venkatraman S, Surendiran B, Kumar PAR (2019) Spam e-mail classification for the internet of things environment using semantic similarity approach. J Supercomput. https://doi.org/10.1007/s11227-019-02913-7
Zhao S, Zhang Y, Yu L, Cheng B, Ji Y, Chen J (2015) A multidimensional resource model for dynamic resource matching in internet of things. Concurr Comput Pract Exp 27(8):1819–1843
Yang J, Li J, Liu S (2017) A new algorithm of stock data mining in internet of multimedia things. J Supercomput. https://doi.org/10.1007/s11227-017-2195-3
Guha S, Meyerson A, Mishra N, Motwani R, O’Callaghan L (2003) Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng 15(3):515–528
O’callaghan L, Mishra N, Meyerson A, Guha S, Motwani R (2002) Streaming-data algorithms for high-quality clustering. In: 18th IEEE International Conference on Data Engineering, pp 685–694
Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: 29th International Conference on Very Large Data Bases (VLDB 2003), pp 81–92
Aggarwal CC, Han J, Wang J, Yu PS (2004) A framework for projected clustering of high dimensional data streams. In: 13th International Conference on Very large Data Bases (VLDB 2004), pp 852–863
Cao F, Estert M, Qian W, Zhou A (2009) Density-based clustering over an evolving data stream with noise. In: SIAM Conference on Data Mining, pp 328–339
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976
Zheng Y, Chen P (2013) Clustering based on enhanced \(\alpha\)-expansion move. IEEE Trans Knowl Data Eng (TKDE) 25(10):2206–2216
Tappen MF, Freeman WT (2003) Comparison of graph cuts with belief propagation for stereo, using identical mrf parameters. In: 9th IEEE International Conference on Computer Vision, pp 900–906
Kolmogorov V, Rother C. Comparison of energy minimization algorithms for highly connected graphs. In: European Conference on Computer Vision (ECCV), vol 3952
Wang K, Zhang J, Li D, Zhang X, Guo T (2008) Adaptive affinity propagation clustering. In: arXiv preprint axXiv:0805.1096
Bi A, Chung F, Wang S, Jiang Y, Huang C (2016) Bayesian enhanced \(\alpha\)-expansion move clustering with loose link constraints. Neurocomputing 194:288–300
Bi A, Wang S (2016) Incremental enhanced \(\alpha\)-expansion move for large data: a probability regularization perspective. J Mach Learn Cybernetics. https://doi.org/10.1007/s13042-016-0532-0
Abbasi M, Rafiee M (2019) A calibrated asymptotic framework for analyzing packet classification algorithms on GPUs. Supercomput 75:6574–6611
Jiang Y, Deng Z, Chung FL, Wang G, Qian P, Choi KS, Wang S (2017) Recognition of epileptic eeg signals using a novel multi-view tsk fuzzy system. IEEE Trans Fuzzy Syst 25(1):3–20
Jiang Y, Chung FL, Wang S, Deng Z, Wang J, Qian P (2015) Collaborative fuzzy clustering from multiple weighted views. IEEE Trans Cybern 45(4):688–701
Jiang Y, Chung FL, Ishibuchi H, Deng Z, Wang S (2015) Multitask tsk fuzzy system modeling by mining intertask common hidden structure. IEEE Trans Cybern 45(3):548–561
Xia K, Yin H, Qian P, Jiang Y, Wang S (2019) Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal ct images. IEEE Access 7:96349–96358
Xia KJ, Yin HS, Zhang YD (2019) Deep semantic segmentation of kidney and space-occupying lesion area based on scnn and resnet models combined with sift-flow algorithm. Med Syst 43(1):2:1–2:12
Jiang Y, Zhao K, Xia K, Xue J, Zhou L, Ding Y, Qian P (2019) A novel distributed multitask fuzzy clustering algorithm for automatic MR brain image segmentation. Med Syst 43(5):118:1–118:9
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 61702225 and 61772241, by the Natural Science Foundation of Jiangsu Province under Grant BK20160187, by the 2018 Six Talent Peaks Project of Jiangsu Province under Grant XYDXX-127, by the Science and Technology demonstration project of social development of Wuxi under Grant WX18IVJN002, by the 2018 Natural Science Foundation of Jiangsu Higher Education Institutions under Grant 18KJB5200001.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jiang, Y., Bi, A., Xia, K. et al. Exemplar-based data stream clustering toward Internet of Things. J Supercomput 76, 2929–2957 (2020). https://doi.org/10.1007/s11227-019-03080-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-03080-5