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To address these problems, a new fuzzy clustering algorithm for interactive multi-sensor probabilistic data is proposed in this paper. The optimal hierarchical fusion algorithm with no prior knowledge is used to sort the sensors used for fusion according to the quality and the importance of information. The fusion of the first layer is the fusion of probabilistic data of two interactive sensors. The fusion of the second layer is the fusion of the fusion results of the first layer and the probability data of the other sensor to obtain the final fusion results. On this basis, the fuzzy C mean clustering algorithm is proposed to cluster the interactive multi-sensor probabilistic data. Wireless sensor networks are dynamic, and it is difficult to determine the number of classes beforehand. Subtraction clustering algorithm is used to adaptively determine the number of classes and the initial cluster center though building mountain function as the data density index. Thus, the convergence speed of the algorithm is accelerated and the local optimum is avoided. Experimental results show that the proposed algorithm has high clustering accuracy and good scalability.<\/jats:p>","DOI":"10.3233\/jifs-169747","type":"journal-article","created":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T16:40:20Z","timestamp":1530290420000},"page":"4267-4275","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Fuzzy clustering algorithm of interactive multi-sensor probabilistic data"],"prefix":"10.1177","volume":"35","author":[{"given":"Chengxi","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Suzhou Vocational University, Suzhou, China"}]},{"given":"K.F.","family":"Kim","sequence":"additional","affiliation":[{"name":"United States Census Bureau, Center for Statistical Research and Methodology (CSRM), Washington, DC, USA"}]}],"member":"179","published-online":{"date-parts":[[2018,6,28]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2526683"},{"key":"e_1_3_1_3_2","first-page":"219","article-title":"Application of fuzzy clustering and piezoelectric chemical sensor array for investigation on organic compounds","volume":"398","author":"Bark\u00f3 G.","year":"2015","unstructured":"Bark\u00f3G., AbonyiJ. and HlavayJ., Application of fuzzy clustering and piezoelectric chemical sensor array for investigation on organic compounds, Analyticachimicaacta398 (2015), 219\u2013226.","journal-title":"Analyticachimicaacta"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.03.006"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11071-015-2372-y"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2015.02.042"},{"key":"e_1_3_1_7_2","first-page":"380","article-title":"An improved image segmentation algorithm and simulation based on fuzzy clustering","volume":"32","author":"Zhang J.","year":"2015","unstructured":"ZhangJ. and FanH.H., An improved image segmentation algorithm and simulation based on fuzzy clustering, Computer Simulation32 (2015), 380\u2013383.","journal-title":"Computer Simulation"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-spr.2016.0306"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2015.04.009"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2016.0282"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2016.05.009"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/00387010.2017.1317271"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.04.005"},{"key":"e_1_3_1_14_2","first-page":"693","article-title":"Multi-dimensional fuzzy clustering image segmentation algorithm based on kernel metric and local information","volume":"51","author":"Wang S.","year":"2015","unstructured":"WangS., LanD.I. and LiangJ., Multi-dimensional fuzzy clustering image segmentation algorithm based on kernel metric and local information, Electronics Letters51 (2015), 693\u2013695.","journal-title":"Electronics Letters"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-015-0997-x"},{"issue":"1","key":"e_1_3_1_16_2","first-page":"54","article-title":"A novel image retrieval based on rectangular spatial histograms of visual words","volume":"45","author":"Mehmood Z.","year":"2018","unstructured":"MehmoodZ., AnwarS.M. and AltafM., A novel image retrieval based on rectangular spatial histograms of visual words, Kuwait Journal of Science45(1) (2018), 54\u201369.","journal-title":"Kuwait Journal of Science"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1080\/09720502.2014.927612"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/09720529.2016.1183314"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.3934\/dcdss.2017051"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/w9110846"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1515\/pomr-2017-0110"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.21042\/AMNS.2017.1.00014"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.21042\/AMNS.2017.1.00023"}],"container-title":["Journal of Intelligent &amp; 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