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In order to realize fast clustering of large data business request data and improve the accuracy of clustering. This paper presents a data fuzzy clustering algorithm based on Adaptive Incremental learning time series. The algorithm defines large data clustering in time series, and the incremental time series clustering method is used. Firstly, the complexity of network data is reduced by data compression, and then time series data clustering based on service time similarity is carried out. In this paper, the time series fuzzy clustering algorithm based on Adaptive Incremental Learning inherits the clustering structure information obtained by previous clustering. Initialize the current clustering process, and then search the outlier samples in the current data block adaptively without setting parameters. Automatically create new clusters from outlier samples, and finally check empty cluster recognition. Identification determines whether certain clusters need to be deleted to ensure the efficiency of subsequent cluster processes. The experimental results show that the algorithm has good clustering accuracy and efficiency for isochronous and unequal time series.<\/jats:p>","DOI":"10.3233\/jifs-179624","type":"journal-article","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T16:25:55Z","timestamp":1580228755000},"page":"3991-3998","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Fuzzy clustering algorithm for time series based on adaptive incremental learning"],"prefix":"10.1177","volume":"38","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"College of Automation Science and Electrical Engineering, Beihang University, Beijing, China"},{"name":"Institute of Software Chinese Academy of Science, Beijing, China"}]},{"given":"Xiaohui","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]},{"given":"Mingye","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]}],"member":"179","published-online":{"date-parts":[[2020,1,27]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-017-0663-2"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2017.01.019"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2372060"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.neucom.2016.09.054","article-title":"Supervised adaptive incremental clustering for data stream of chunks","volume":"219","author":"Zheng L.","year":"2016","unstructured":"ZhengL., HuoH. and GuoY., Supervised adaptive incremental clustering for data stream of chunks, Neurocomputing 219(C) (2016), 502\u2013517.","journal-title":"Neurocomputing"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12271"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.12.016041"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12293-016-0196-z"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2018.05.072"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.03.068"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2017.2657607"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/e18050182"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-2102-x"},{"key":"e_1_3_1_14_2","first-page":"1","article-title":"Fast and accurate hierarchical clustering based on growing multilayer topology training","author":"Cheung Y.M.","year":"2018","unstructured":"CheungY.M. and ZhangY., Fast and accurate hierarchical clustering based on growing multilayer topology training, IEEE Transactions on Neural Networks and Learning Systems (2018), 1\u201315.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_15_2","first-page":"1","article-title":"Shape preserving incremental learning for power systems fault detection","author":"Jose C.","year":"2018","unstructured":"JoseC., CarlosS. and MostafaG., Shape preserving incremental learning for power systems fault detection, IEEE Control Systems Letters (2018), 1\u20131.","journal-title":"IEEE Control Systems Letters"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.11.024"},{"issue":"6","key":"e_1_3_1_17_2","first-page":"1096","article-title":"None, A load-balancing self-organizing incremental neural network","volume":"25","year":"2016","unstructured":"None, A load-balancing self-organizing incremental neural network, IEEE Transactions on Neural Networks and Learning Systems 25(6) (2016), 1096\u20131105.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-015-1192-x"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-016-9588-7"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2017.2692203"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.01.057"}],"container-title":["Journal of Intelligent &amp; 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