{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:33:11Z","timestamp":1777703591815,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2017,2,24]],"date-time":"2017-02-24T00:00:00Z","timestamp":1487894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,2,24]]},"abstract":"<jats:p>Anomaly detection is an important task for applications involving Big Data. Comparing with traditional method, anomaly detection in Big Data confronts growing amounts of data with high dimensionality and complex structures, which require more real-time analysis. This paper presents a fuzzy input-output system for anomalous data using electronic consumer records (ECR), a trapezium-cloud-map-filtration (TCMF) framework and a value mining model. ECRs are used to add or remove criteria based on consumers\u2019 consumption. In addition, MapReduce framework and trapezium clouds generated from each subsample are aggregated by using the aggregated trapezium cloud as a filter for each subsample. Then, a fuzzy logic-based value mining model is proposed based on Takagi-Sugeno model (T-S model) and trapezium clouds. This paper establishes a system that can improve decision-making accuracy by filtering large-scale data, and an illustrative example using a hotel booking situation is presented to verify the validity and feasibility of the proposed model. Finally, a comparative analysis is conducted between the proposed approach and existing methods.<\/jats:p>","DOI":"10.3233\/jifs-16254","type":"journal-article","created":{"date-parts":[[2017,2,28]],"date-time":"2017-02-28T11:35:44Z","timestamp":1488281744000},"page":"2295-2308","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["An evolving Takagi-Sugeno model based on aggregated trapezium clouds for anomaly detection in large datasets"],"prefix":"10.1177","volume":"32","author":[{"given":"Meng-Xian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Business, Central South University, Changsha, China"},{"name":"School of Mathematics and Computational Science, Hunan City University, Yiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian-Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Business, Central South University, Changsha, China"},{"name":"Key Laboratory of Hunan Province for Mobile Business Intelligence, Changsha, China"},{"name":"Mobile E-Business Collaborative Innovation Center of Hunan Province, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,2,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwt032"},{"key":"e_1_3_2_3_2","unstructured":"RajaramanA. LeskovecJ. and UllmanJ. Mining of massive datasets Cambridge University Press 2011."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11749-010-0197-z"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000000590"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.109"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1197962"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2014.2322385"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2008.00674.x"},{"key":"e_1_3_2_10_2","article-title":"Data Preparation for data mining","volume":"1","author":"Pyle D.","year":"1999","unstructured":"PyleD., Data Preparation for data mining, Morgan Kaufmann 1 (1999).","journal-title":"Morgan Kaufmann"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:AIRE.0000045502.10941.a9"},{"issue":"3","key":"e_1_3_2_13_2","first-page":"237","article-title":"Distance-based outliers: Algorithms and applications","volume":"8","author":"Knorr E.M.","year":"2000","unstructured":"KnorrE.M., NgR.T. and TucakovV., Distance-based outliers: Algorithms and applications, The VLDB Journal-The International Journal on Very Large Data Bases 8(3-4) (2000), 237\u2013253.","journal-title":"The VLDB Journal-The International Journal on Very Large Data Bases"},{"key":"e_1_3_2_14_2","first-page":"831","article-title":"Outlier detection in axis-parallel subspaces of high dimensional data","author":"Kriegel H.P.","year":"2009","unstructured":"KriegelH.P., Kr\u00f6gerP., SchubertE. and ZimekA., Outlier detection in axis-parallel subspaces of high dimensional data, In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2009, pp. 831\u2013838.","journal-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11161"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(03)00003-5"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/2733381"},{"key":"e_1_3_2_18_2","first-page":"605","article-title":"A fuzzy logic-based method for outliers detection","author":"Cateni S.","year":"2007","unstructured":"CateniS., CollaV. and VannucciM., A fuzzy logic-based method for outliers detection, In: Artificial Intelligence and Application, 2007, pp. 605\u2013610.","journal-title":"Artificial Intelligence and Application"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CICYBS.2011.5949392"},{"key":"e_1_3_2_20_2","first-page":"292","article-title":"Automated detection of outliers in real-world data","author":"Last M.","year":"2001","unstructured":"LastM. and KandelA., Automated detection of outliers in real-world data, In: Proc of the Second International Conference on Intelligent Technologies, 2001, pp. 292\u2013301.","journal-title":"Proc of the Second International Conference on Intelligent Technologies"},{"key":"e_1_3_2_21_2","first-page":"192","article-title":"Fuzzy clustering-based approach for outlier detection","author":"Al-Zoubi M.D.B.","year":"2008","unstructured":"Al-ZoubiM.D.B., AliA.-D. and YahyaA.A., Fuzzy clustering-based approach for outlier detection, In: Proc of the 9th WSEAS Int Conf on Applications of Computer Engineering, 2008, pp. 192\u2013197.","journal-title":"Proc of the 9th WSEAS Int Conf on Applications of Computer Engineering"},{"key":"e_1_3_2_22_2","unstructured":"IshibuchiH. NakashimaT. and NiiM. Classification and modeling with linguistic information granules: Advanced approaches to linguistic Data Mining Springer Science & Business Media 2006."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1985.6313399"},{"key":"e_1_3_2_24_2","first-page":"443","article-title":"Electricity consumer classification using artificial intelligence","author":"Lo K.L.","year":"2004","unstructured":"LoK.L. and ZakariaZ., Electricity consumer classification using artificial intelligence, Universities Power Engineering Conference, 2004, UPEC 2004 39th International 1, 2004, pp. 443\u2013447.","journal-title":"Universities Power Engineering Conference, 2004, UPEC 2004 39th International 1"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2009.08.075"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/AINA.2010.36"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/1529282.1529617"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.04.078"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2014.01.015"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.3390\/su8070630"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1080\/18756891.2016.1204116"},{"issue":"11","key":"e_1_3_2_33_2","first-page":"2859","article-title":"Multiple criteria group decision making method based on intuitionistic normal cloud by Monte Carlo simulation","volume":"33","author":"Wang J.Q.","year":"2013","unstructured":"WangJ.Q. and YangW.E., Multiple criteria group decision making method based on intuitionistic normal cloud by Monte Carlo simulation, System Engineering Theory and Practice 33(11) (2013), 2859\u20132865.","journal-title":"System Engineering Theory and Practice"},{"key":"e_1_3_2_34_2","article-title":"Multi-criteria decision-making approach based on gray linguistic weighted Bonferroni mean operator","author":"Tian Z.P.","year":"2015","unstructured":"TianZ.P., WangJ., WangJ.Q. and ChenX.H., Multi-criteria decision-making approach based on gray linguistic weighted Bonferroni mean operator, International Transactions in Operational Research (2015). doi: 10.1111\/itor.12220","journal-title":"International Transactions in Operational Research"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10726-012-9316-4"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.80"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2000.906164"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10726-014-9385-7"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-016-9394-8"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-016-9417-5"},{"issue":"5","key":"e_1_3_2_41_2","first-page":"1235","article-title":"Application of trapezium-cloud model in conception division and conception exaltation","volume":"29","author":"Jiang J.B.","year":"2008","unstructured":"JiangJ.B., LiangJ.R., JiangW. and GuZ.P., Application of trapezium-cloud model in conception division and conception exaltation, Computer Engineering and Design 29(5) (2008), 1235\u20131240.","journal-title":"Computer Engineering and Design"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2014.2317500"},{"key":"e_1_3_2_43_2","first-page":"1","article-title":"A cloud-based framework for home-diagnosis service over big medical data","volume":"102","author":"Lin W.M.","year":"2014","unstructured":"LinW.M., DouW.C., ZhouZ.J. and LiuC., A cloud-based framework for home-diagnosis service over big medical data, The Journal of Systems and Software 102 (2014), 1\u201315.","journal-title":"The Journal of Systems and Software"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1108\/EC-08-2015-0258"},{"key":"e_1_3_2_45_2","article-title":"Multi-criteria decision making approaches based on distance measures for linguistic hesitant fuzzy sets","author":"Zhou H.","year":"2016","unstructured":"ZhouH., WangJ.Q. and ZhangH.Y., Multi-criteria decision making approaches based on distance measures for linguistic hesitant fuzzy sets, Journal of the Operational Research Society (2016). doi: 10.1057\/jors.2016.41","journal-title":"Journal of the Operational Research Society"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2016.07.023"},{"key":"e_1_3_2_47_2","article-title":"An Interval type-2 fuzzy likelihood-based MABAC approach and its application in selecting hotels on a tourism website","author":"Yu S.M.","year":"2016","unstructured":"YuS.M., WangJ. and WangJ.Q., An Interval type-2 fuzzy likelihood-based MABAC approach and its application in selecting hotels on a tourism website, International Journal of Fuzzy Systems (2016). doi: 10.1007\/s40815-016-0217-6.","journal-title":"International Journal of Fuzzy Systems"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-16254","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-16254","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-16254","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:04Z","timestamp":1777455544000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-16254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,24]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017,2,24]]}},"alternative-id":["10.3233\/JIFS-16254"],"URL":"https:\/\/doi.org\/10.3233\/jifs-16254","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,24]]}}}