The early warning classification plays an important role in the emergency management of cluster supply chain. This paper proposed the high-dimensional datastream evolutionary clustering algorithm of early warning classification for cluster supply chain emergency based on cloud model. It solved the bottleneck problem of early warning classification of cluster supply chain emergency with the high-dimensional datastream and composite uncertainty characteristics. The cloud model generation algorithm of early warning summary is used to generate the early warning summary data based on the multiple data fusion method. The evolutionary datastream clustering algorithm of early warning classification is used to dynamically forecast the harming degree of cluster supply chain emergency based on time decaying model and sliding window model. Compared to other similar algorithms, the algorithm proposed in this paper increased the classification accuracy by 92.6% while reduced operation time by 66.7%. The algorithm can provide more accurate decision supports for design and implementation of emergency preplan of cluster supply chain emergency. The feasibility of this algorithm has been demonstrated by multiple experiments conducted on the algorithm.
AminiA., SaboohiH. and HerawanT., MuDi-Stream: A multi density clustering algorithm for evolving data stream, Journal of Network and Computer Applications59 (2016), 370–385.
2.
AminiA., TehY.W. and SaboohiH., On density-based data streams clustering algorithms: A survey, Journal of Computer Science and Technology29(1) (2014), 116–141.
3.
ForestieroA., PizzutiC. and SpezzanoG., A single pass algorithm for clustering evolving data streams based on swarm intelligence, Data Mining and Knowledge Discovery26(1) (2013), 1–26.
4.
WuA.Y., MaZ.G. and ZengG.P., Set pair fuzzy decision method based on cloud model, Chinese Journal of Electronics25(2) (2016), 215–219.
5.
GaoB.H., JiangY.L., An inventory strategy with emergency order in cluster supply chain, 2nd International Conference on Teaching and Computational Science (ICTCS), Shenzhen, China, 2014, pp. 44–47.
6.
SarahB.O., HayfaZ., MariagraziaD. and SlimH., An agent-based decision support system for resources’ scheduling in emergency supply chains, Control Engineering Practice59 (2017), 27–43.
7.
YanB., LiuY.P. and XiaC., Replenishment decision making and coordination contract of cluster supply chain in a centralized VMI and TPL system, Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice35(8) (2015), 1968–1982.
8.
YangB., MaZ.H., XuB., Research on the earthquake disaster grade prediction model based on numerical simulation and big data fuzzy clustering algorithm, 3rd International Conference on Advances in Social Science Humanities, and Management (ASSHM), Kyoto, Japan, 2015, pp. 2260–2267.
9.
WangC.D., LaiJ.H. and HuangD., Svstream: A support vector-based algorithm for clustering data streams, IEEE Transactions on Knowledge and Data Engineering25(6) (2013), 1410–1424.
10.
ZhangC.S., ZhangM.M. and ZhangW.X., Reconstruction of measurable three-dimensional point cloud model based on large-scene archaeological excavation sites, Journal of Electronic Imaging26(1) (2017), 172–183.
11.
WangD., ZengD.B. and SinghV.J.P., A multidimension cloud model-based approach for water quality assessment, Environmental Research149 (2016), 113–121.
12.
PachecoF., CerradaM., SánchezR.V., CabreraD., LiC. and ValenteD.O.J., Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery, Expert Systems with Applications71 (2017), 69–86.
13.
YanF. and XuK., Application of a cloud model-set pair analysis in hazard assessment for biomass gasification stations, Plos One12(1) (2017), 120–131.
14.
QumerG.A., NathanP., DeanL. and VinhL.P., IoT-enabled emergency information supply chain architecture for elderly people: The australian context, Information Systems58 (2016), 75–86.
15.
PanG., LiK.L. and OuyangA.J., A hybrid clustering algorithm combining cloud model iwo and k-means, International Journal of Pattern Recognition and Artificial Intelligence28(6) (2014), 68–77.
16.
JiangH., RuanJ.H., Study on inventory early-warning
in supply chains based on rough sets and bp neural network, International Conference on Risk Management and Engineering Management, Beijing, China, 2008, pp. 13–19.
17.
LiH.K. and LiC., Coordination analysis on cluster supply chains under same level across-chain inventory collaboration mechanism, Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems21(12) (2015), 3282–3291.
18.
WeiH.S., JunH.H. and YueM.J., Research on cluster supply chain risk evaluation base on improved multi-level fuzzy comprehensive evaluation method, International Conference on Management Science and Engineering3 (2010), 473–478.
19.
SilvaJ.D.A., HruschkaE.D.R. and GamaJ., An evolutionary algorithm for clustering data streams with a variable number of clusters, Expert Systems with Applications67 (2017), 228–238.
20.
JiangJ.F., HanG.Q., ZhuC.S., ChanS. and RodriguesJ.J.P.C., A trust cloud model for underwater wireless sensor networks, IEEE Communications Magazine55(3) (2017), 110–116.
21.
LiuJ.G., Dynamic predicting in business crisis based on multi-dimensional ewma control chart, Industrial Engineering and Management15(5) (2010), 70–75, 86.
22.
SongJ.K. and LiJ.Z., Coal security forewarning based on pca-ar and k-means clustering, Journal of Shandong University of Science and Technology27(2) (2008), 105–108.
23.
MarianneJ., JoakimK., TinaR., OzlemE., SveinJ.H. and PeterB., Integrating supply chains for emergencies and ongoing operations in unhcr, Journal of Operations Management45 (2016), 57–72.
24.
BarddalJ.P., GomesH.M., EnembreckF. and BarthèsJ.P., SNCStream (+): Extending a high quality true anytime data stream clustering algorithm, Information Systems62 (2016), 60–73.
25.
HeJ.P., GaoQ. and ShiY.Q., A multi-hierarchical comprehensive evaluation method of dam safety based on cloud model, Systems Engineering-Theory and Practice36(11) (2016), 2977–2983.
26.
ShenJ.Q., ZhangN. and LiX.Z., Cluster supply chain collaborative procurement model optimization and simulation implementation based on agent, Lecture Notes in Electrical Engineering272(3) (2014), 255–264.
27.
ChouJ.S., ThedjaJ.L. and PratamaP., Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems, Automation in Construction68 (2016), 65–80.
28.
ChenJ.Y. and HeH.H., A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data, Information Sciences345 (2016), 271–293.
29.
WangJ.Z., NiuT. and WangR., Research and application of an air quality early warning system based on a modifiedleast squares support vector machine and a cloud model, International Journal of Environmental Research and Public Health14(3) (2017), 210–221.
30.
ChenK. and YangL., Random yield and coordination mechanisms of a supply chain with emergency backup sourcing, International Journal of Production Research52(16) (2014), 4747–4767.
31.
LiL., JunX. and PengQ.Y., Design and control mechanism research of cluster supply chain risk system, Science and Technology Management Research1 (2008), 245–247.
32.
TelesL.O., FernandesM. and AmorimJ., Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM), Aquatic Toxicology165 (2015), 241–248.
33.
WuL.Q., ZuoC., ZhangH.Q. and LiuZ., Bimodal fruit fly optimization algorithm based on cloud model learning, Soft Computing21(7) (2017), 1877–1893.
34.
WeiL.Y. and PengW.C., An incremental algorithm for clustering spatial data streams: Exploring temporal locality, Knowledge and Information Systems37(2) (2013), 453–483.
35.
HassaniM., KimY., ChoiS. and SeidlT., Subspace clustering of data streams: New algorithms and effective evaluation measures, Journal of Intelligent Information Systems45(3) (2015), 319–335.
36.
WangM.Q., ZhuQ.K., ZhuY. and JinJ.L., Shrinkage-swelling evaluation of untreated and lime-treated expansive clays based on the asymmetric connection cloud model, Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering25(1) (2017), 162–170.
37.
ShanM.Y., ZhangR.L. and ZhangL.H., Combinatorial clustering algorithm of quantum-behaved particle swarm optimization and cloud model, Mathematical Problems in Engineering3 (2013), 1–11.
38.
ZhangQ., XiaoY., SuoJ.F., ShiJ., YuJ.H., GuoY., WangY.Y. and ZhengH., Sonoelastomics for breast tumor classification: A radiomics approach with clustering-based feature selection on sonoelastography, Ultrasound in Medicine and Biology43(5) (2017), 1058–1069.
39.
ZhangR.L., ShanM.Y. and LiuX.H., A novel fuzzy hybrid quantum artificial immune clustering algorithm based on cloud model, Engineering Applications of Artificial Intelligence35 (2014), 1–13.
40.
AbdullahiS., SchardtM. and PretzschH., An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data - A case study in complex temperate forest stands, International Journal of Applied Earth Observation and Geoinformation57 (2017), 36–48.
41.
ZhangS.B. and XuC.X., Study on the trust evaluation approach based on cloud model, Chinese Journal of Computers36(2) (2013), 422–431.
42.
DingS., WuF., QianJ., JiaH. and JinF., Research on data stream clustering algorithms, Artificial Intelligence Review43(4) (2015), 593–600.
43.
DingS., ZhangJ., JiaH. and QianJ., An adaptive density data stream clustering algorithm, Cognitive Computation8(1) (2016), 30–38.
44.
MaS.D., ZhangH.Z. and YangG.Q., Target threat level assessment based on cloud model under fuzzy and uncertain conditions in air combat simulation, Aerospace Science and Technology67 (2017), 49–53.
45.
LaohakiatS., PhimoltaresS. and LursinsapC., A clustering algorithm for stream data with LDA-based unsupervised localizeddimension reduction, Information Sciences381 (2017), 104–123.
46.
ChenS.N., ChenB.Y., WeiH. and YeL., Application of improved mixed half cloud model in probability distribution fitting for irregular wind speed, Dianli Zidonghua Shebei/Electric Power Automation Equipment37(4) (2017), 60–66.
47.
KimS.S. and AhnH.K., An improved data stream algorithm for clustering, Computational Geometry-Theory and Applications48(9) (2015), 635–645.
48.
WangS.S., WangH.Q. and TangQ.F., Research on risk assessment index system of cluster supply chain, Science and Technology Management Research7 (2010), 224–226.
49.
ChangT.C. and WangH., A multi criteria group decision-making model for teacher evaluation in higher education based on cloud model and decision tree, Eurasia Journal of Mathematics Science and Technology Education12(5) (2016), 1243–1262.
50.
HuT., JiaoY.W., WangH.B., HouD.T., YuD.J. and ChenX.L., The efficiency evaluation of information networks based on interval-based cloud model, Systems Engineering-Theory and Practice37(2) (2017), 478–485.
51.
YuX.G. and YuX.P., On unsupervised clustering algorithm based on distance and density, Computer Applications and Software27(7) (2010), 122–125.
52.
BiW.J., CaiM.L. and LiuM.Q., A big data clustering algorithm for mitigating the risk of customer churn, IEEE Transactions on Industrial Informatics12(3) (2016), 1270–1281.
53.
ZhouX.Q., LiJ., LiuC.L. and LiB.X., Optimization and parallel allocation of cross-chain orders in cluster supply chain with Lagrange algorithm, Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems21(8) (2015), 2179–2190.
54.
JiX.W., WengW.G. and NiS.J., Warning classification model for public emergencies, Journal of Tsinghua University (Sci and Tech)48(8) (2008), 1252–1255.
55.
XueX., LiuZ.Z. and HuangB.Q., Enterprise service composition method for cluster supply chain, Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems20(10) (2014), 2599–2608.
56.
WangY. and ZhangH.F., An analysis of cluster supply chain risk:A formation mechanism perspective, Energy Education Science and Technology Part A: Energy Science and Research32(6) (2014), 5413–5430.
57.
ChenY.X. and SongY., Emergency response capability assessment of emergency supply chaincoordination mechanism based on hesitant fuzzy information, International Journal of Simulation Modelling13(4) (2014), 485–496.
58.
XuY., YuL.Y. and LinC.Y., Research on the warning classification of university public crisis by an improved fuzzy topsis based on alpha level sets, Journal of Shanghai University (Natural Science)22(1) (2016), 1–9.
59.
WangY.Z., JinX.L. and ChengX.Q., Network big data: Present and future, Chinese Journal of Computers36(6) (2013), 1125–1138.
60.
WangZ.X., GuoY. and ZhangL.C., Early warning of sudden events based on the cloud model, Journal of Information Engineering University13(1) (2012), 120–123, 128.
