{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:35Z","timestamp":1740122675841,"version":"3.37.3"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012334","name":"Graduate Scientific Research and Innovation Foundation of Chongqing","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012334","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10489-023-04593-6","type":"journal-article","created":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T09:01:56Z","timestamp":1687078916000},"page":"21961-21983","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A double-weighted outlier detection algorithm considering the neighborhood orientation distribution of data objects"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1102-8784","authenticated-orcid":false,"given":"Qiang","family":"Gao","sequence":"first","affiliation":[]},{"given":"Qin-Qin","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Zhong-Yang","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Yu-Fang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yu-Qin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,18]]},"reference":[{"key":"4593_CR1","doi-asserted-by":"crossref","unstructured":"Gao X, Yu J, Zha S, Fu S, Xue B, Ye P, Huang Z, Zhang G (2022) An ensemble-based outlier detection method for clustered and local outliers with differential potential spread loss. Knowledge-Based Systems 110003","DOI":"10.1016\/j.knosys.2022.110003"},{"key":"4593_CR2","doi-asserted-by":"crossref","unstructured":"Hawkins D (1980) Identification of outliers. Chapman and Hall","DOI":"10.1007\/978-94-015-3994-4"},{"key":"4593_CR3","doi-asserted-by":"publisher","unstructured":"Mandhare HC, Idate SR (2017) A comparative study of cluster based outlier detection, distance based outlier detection and density based outlier detection techniques. In: 2017 international conference on intelligent computing and control systems (ICICCS). pp 931\u2013935. https:\/\/doi.org\/10.1109\/ICCONS.2017.8250601","DOI":"10.1109\/ICCONS.2017.8250601"},{"key":"4593_CR4","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.patcog.2017.09.037","volume":"74","author":"R Domingues","year":"2018","unstructured":"Domingues R, Filippone M, Michiardi P, Zouaoui J (2018) A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recogn 74:406\u2013421. https:\/\/doi.org\/10.1016\/j.patcog.2017.09.037","journal-title":"Pattern Recogn"},{"key":"4593_CR5","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.neucom.2017.02.039","volume":"241","author":"B Tang","year":"2017","unstructured":"Tang B, He H (2017) A local density-based approach for outlier detection. Neurocomputing 241:171\u2013180. https:\/\/doi.org\/10.1016\/j.neucom.2017.02.039","journal-title":"Neurocomputing"},{"key":"4593_CR6","doi-asserted-by":"crossref","unstructured":"Caroline CP, Thomas GS (2001) An outlier detection approach on credit card fraud detection using machine learning: a comparative analysis on supervised and unsupervised learning. In: Intelligence in big data technologies\u2014beyond the hype. pp 125\u2013135","DOI":"10.1007\/978-981-15-5285-4_12"},{"key":"4593_CR7","doi-asserted-by":"publisher","first-page":"116212","DOI":"10.1016\/j.eswa.2021.116212","volume":"191","author":"Y Yang","year":"2002","unstructured":"Yang Y, Fan CJ, Chen L, Xiong HL (2002) IPMOD: An efficient outlier detection model for high-dimensional medical data streams. Expert Syst Appl 191:116212. https:\/\/doi.org\/10.1016\/j.eswa.2021.116212","journal-title":"Expert Syst Appl"},{"key":"4593_CR8","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.asoc.2018.12.029","volume":"76","author":"B Wang","year":"2019","unstructured":"Wang B, Mao Z (2019) Outlier detection based on Gaussian process with application to industrial processes. Appl Soft Comput 76:505\u2013516. https:\/\/doi.org\/10.1016\/j.asoc.2018.12.029","journal-title":"Appl Soft Comput"},{"key":"4593_CR9","doi-asserted-by":"crossref","unstructured":"Lu S, He T, Zhou Q, Wen J, Liu Y, Zhang M(2020) Research on a distribution-outlier detection algorithm based on logistics distribution data. J Phys Confer Ser (6pp) 1624:042002","DOI":"10.1088\/1742-6596\/1624\/4\/042002"},{"key":"4593_CR10","doi-asserted-by":"crossref","unstructured":"Li, Z, Zhao Y, Hu X, Botta N, Ionescu C, Chen GH (2022) ECOD: unsupervised outlier detection using empirical cumulative distribution functions. CoRR arXiv:2201.00382","DOI":"10.2139\/ssrn.4313179"},{"key":"4593_CR11","doi-asserted-by":"publisher","unstructured":"Issac J, W\u00fcthrich M, Cifuentes CG, Bohg J, Trimpe S, Schaal S (2016) Depth-based object tracking using a robust gaussian filter. In: 2016 IEEE international conference on robotics and automation (ICRA). pp 608\u2013615. https:\/\/doi.org\/10.1109\/ICRA.2016.7487184","DOI":"10.1109\/ICRA.2016.7487184"},{"issue":"1","key":"4593_CR12","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.jspi.2009.07.004","volume":"140","author":"X Dang","year":"2010","unstructured":"Dang X, Serfling R (2010) Nonparametric depth-based multivariate outlier identifiers, and masking robustness properties. J Stat Plann Inference 140(1):198\u2013213. https:\/\/doi.org\/10.1016\/j.jspi.2009.07.004","journal-title":"J Stat Plann Inference"},{"key":"4593_CR13","doi-asserted-by":"publisher","first-page":"113215","DOI":"10.1016\/j.eswa.2020.113215","volume":"147","author":"F Angiulli","year":"2020","unstructured":"Angiulli F, Basta S, Lodi S, Sartori C (2020) Reducing distance computations for distance-based outliers. Expert Syst Appl 147:113215. https:\/\/doi.org\/10.1016\/j.eswa.2020.113215","journal-title":"Expert Syst Appl"},{"key":"4593_CR14","unstructured":"Knorr E, Ng R (1997) A unified notion of outliers. Properties and computation"},{"key":"4593_CR15","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1016\/j.procs.2022.01.297","volume":"200","author":"D Muhr","year":"2022","unstructured":"Muhr D, Affenzeller M (2022) Little data is often enough for distance-based outlier detection. Proc Comput Sci 200:984\u2013992. https:\/\/doi.org\/10.1016\/j.procs.2022.01.297","journal-title":"Proc Comput Sci"},{"key":"4593_CR16","doi-asserted-by":"publisher","first-page":"117988","DOI":"10.1016\/j.eswa.2022.117988","volume":"207","author":"K Li","year":"2022","unstructured":"Li K, Gao X, Fu S, Diao X, Ye P, Xue P, Yu J, Huang Z (2022) Robust outlier detection based on the changing rate of directed density rati. Expert Syst Appl 207:117988. https:\/\/doi.org\/10.1016\/j.eswa.2022.117988","journal-title":"Expert Syst Appl"},{"key":"4593_CR17","doi-asserted-by":"crossref","unstructured":"Ranjan Gaurav K, Prusty Rajanarayan B (2022) A detailed analysis of adaptive kernel density-based outlier detection in volatile time series. In: Machine learning, advances in computing, renewable energy and communication. pp 359\u2013369","DOI":"10.1007\/978-981-16-2354-7_33"},{"key":"4593_CR18","doi-asserted-by":"crossref","unstructured":"Breunig M et al (2000) Lof: identifying density-based local outliers. ACM Sigmod Record","DOI":"10.1145\/342009.335388"},{"key":"4593_CR19","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.knosys.2017.10.009","volume":"139","author":"L Zhang","year":"2018","unstructured":"Zhang L, Lin J, Karim R (2018) Adaptive kernel density-based anomaly detection for nonlinear systems. Knowl-Based Syst 139:50\u201363. https:\/\/doi.org\/10.1016\/j.knosys.2017.10.009","journal-title":"Knowl-Based Syst"},{"key":"4593_CR20","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1016\/j.ins.2022.06.013","volume":"607","author":"A Degirmenci","year":"2022","unstructured":"Degirmenci A, Karal O (2022) Efficient density and cluster based incremental outlier detection in data streams. Inf Sci 607:901\u2013920. https:\/\/doi.org\/10.1016\/j.ins.2022.06.013","journal-title":"Inf Sci"},{"key":"4593_CR21","doi-asserted-by":"crossref","unstructured":"Beulah J, Rene, Nalini M, Irene D, Shiny, Punithavathani D, Shalini (2022) Enhancing detection of R2L attacks by multistage clustering based outlier detection, wireless personal communications","DOI":"10.1007\/s11277-022-09482-8"},{"key":"4593_CR22","doi-asserted-by":"crossref","unstructured":"Lazhar F (2018) Fuzzy clustering-based semi-supervised approach for outlier detection in big text data. Prog Artif Intell 8(6)","DOI":"10.1007\/s13748-018-0165-5"},{"key":"4593_CR23","doi-asserted-by":"publisher","first-page":"5100","DOI":"10.1007\/s10489-021-02399-y","volume":"52","author":"Z Xiong","year":"2022","unstructured":"Xiong Z, Gao Q, Gao Q, Zhang Y, Li L, Zhang M (2022) ADD: a new average divergence difference-based outlier detection method with skewed distribution of data objects. Appl Intell 52:5100\u20135124. https:\/\/doi.org\/10.1007\/s10489-021-02399-y","journal-title":"Appl Intell"},{"key":"4593_CR24","doi-asserted-by":"publisher","first-page":"107256","DOI":"10.1016\/j.knosys.2021.107256","volume":"228","author":"SAN Nozad","year":"2021","unstructured":"Nozad SAN, Haeri MA, Folino G (2021) SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets. Knowl-Based Syst 228:107256. https:\/\/doi.org\/10.1016\/j.knosys.2021.107256","journal-title":"Knowl-Based Syst"},{"issue":"2017","key":"4593_CR25","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.knosys.2017.01.013","volume":"121","author":"J Huang","year":"2017","unstructured":"Huang J, Zhu Q, Yang L, Cheng D, Wu Q (2017) A novel outlier cluster detection algorithm without top-n parameter. Knowl-Based Syst 121(2017):32\u201340. https:\/\/doi.org\/10.1016\/j.knosys.2017.01.013","journal-title":"Knowl-Based Syst"},{"key":"4593_CR26","doi-asserted-by":"publisher","unstructured":"Dashdondov K, Kim MH (2021) Mahalanobis distance based multivariate outlier detection to improve performance of hypertension prediction. Neural Process Lett. https:\/\/doi.org\/10.1007\/s11063-021-10663-y","DOI":"10.1007\/s11063-021-10663-y"},{"key":"4593_CR27","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.patcog.2017.09.037","volume":"74","author":"R Domingues","year":"2018","unstructured":"Domingues R, Filippone M, Michiardi P, Zouaoui J (2018) A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recogn 74:406\u2013421. https:\/\/doi.org\/10.1016\/j.patcog.2017.09.037","journal-title":"Pattern Recogn"},{"key":"4593_CR28","doi-asserted-by":"crossref","unstructured":"Tang J, Chen Z, Fu A, Cheung D (2002) Enhancing effectiveness of outlier detections for low density patterns. Knowledge discovery and data mining, 535\u2013548","DOI":"10.1007\/3-540-47887-6_53"},{"key":"4593_CR29","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/11731139_68","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"W Jin","year":"2006","unstructured":"Jin W, Tung AKH, Han J, Wang W (2006) Ranking outliers using symmetric neighborhood relationship. Advances in Knowledge Discovery and Data Mining. Springer, Berlin Heidelberg, pp 577\u2013593"},{"key":"4593_CR30","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.neucom.2017.02.039","volume":"241","author":"B Tang","year":"2017","unstructured":"Tang B, He B (2017) A local density-based approach for outlier detection. Neurocomputing 241:171\u2013180. https:\/\/doi.org\/10.1016\/j.neucom.2017.02.039","journal-title":"Neurocomputing"},{"key":"4593_CR31","doi-asserted-by":"publisher","first-page":"103988","DOI":"10.1016\/j.ijmedinf.2019.103988","volume":"132","author":"CH Lin","year":"2019","unstructured":"Lin CH, Hsu KC, Johnson KR, Luby M, Fann YC (2019) Applying density-based outlier identifications using multiple datasets for validation of stroke clinical outcomes. Int J Med Informatics 132:103988. https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.103988","journal-title":"Int J Med Informatics"},{"key":"4593_CR32","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.knosys.2014.03.001","volume":"63","author":"J Ha","year":"2014","unstructured":"Ha J, Seok S, Lee JS (2014) Robust outlier detection using the instability factor. Knowl-Based Syst 63:15\u201323. https:\/\/doi.org\/10.1016\/j.knosys.2014.03.001","journal-title":"Knowl-Based Syst"},{"key":"4593_CR33","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.ijleo.2017.09.116","volume":"154","author":"S Zhang","year":"2018","unstructured":"Zhang S, Wan J (2018) Weight-based method for inside outlier detection. Optik 154:145\u2013156. https:\/\/doi.org\/10.1016\/j.ijleo.2017.09.116","journal-title":"Optik"},{"key":"4593_CR34","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.patrec.2016.05.007","volume":"80","author":"Q Zhu","year":"2016","unstructured":"Zhu Q, Feng J, Huang J (2016) Natural neighbor: A self-adaptive neighborhood method without parameter k. Pattern Recogn Lett 80:30\u201336. https:\/\/doi.org\/10.1016\/j.patrec.2016.05.007","journal-title":"Pattern Recogn Lett"},{"key":"4593_CR35","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.knosys.2015.10.014","volume":"92","author":"J Huang","year":"2016","unstructured":"Huang J, Zhu Q, Yang L, Feng J (2016) A non-parameter outlier detection algorithm based on natural neighbor. Knowl-Based Syst 92:71\u201377. https:\/\/doi.org\/10.1016\/j.knosys.2015.10.014","journal-title":"Knowl-Based Syst"},{"issue":"9","key":"4593_CR36","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1145\/361002.361007","volume":"18","author":"J Bentley","year":"1975","unstructured":"Bentley J (1975) Multidimensional binary search trees used for associated searching. Commun ACM 18(9):509\u2013517","journal-title":"Commun ACM"},{"key":"4593_CR37","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.knosys.2014.03.001","volume":"63","author":"J Ha","year":"2014","unstructured":"Ha J, Seok S, Lee JS (2014) Robust outlier detection using the instability factor. Knowl-Based Syst 63:15\u201323. https:\/\/doi.org\/10.1016\/j.knosys.2014.03.001","journal-title":"Knowl-Based Syst"},{"key":"4593_CR38","doi-asserted-by":"publisher","unstructured":"Wang X, Wang X, Wilkes M (2021) A k-nearest neighbor centroid-based outlier detection method. New Dev Unsupervised Outlier Detection 4:71\u2013112. https:\/\/doi.org\/10.1007\/978-981-15-9519-6-4","DOI":"10.1007\/978-981-15-9519-6-4"},{"issue":"3","key":"4593_CR39","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/S0003-2670(98)00626-6","volume":"388","author":"D Jouan-Rimbaud","year":"1999","unstructured":"Jouan-Rimbaud D, Bouveresse E, Massart D, de Noord O (1999) Detection of prediction outliers and inliers in multivariate calibration. Anal Chim Acta 388(3):283\u2013301. https:\/\/doi.org\/10.1016\/S0003-2670(98)00626-6","journal-title":"Anal Chim Acta"},{"key":"4593_CR40","doi-asserted-by":"publisher","unstructured":"Xi J (2008) Outlier detection algorithms in data mining. In: 2008 2nd international symposium on intelligent information technology application, vol 1. pp 94\u201397. https:\/\/doi.org\/10.1109\/IITA.2008.26","DOI":"10.1109\/IITA.2008.26"},{"key":"4593_CR41","doi-asserted-by":"publisher","first-page":"104907","DOI":"10.1016\/j.knosys.2019.104907","volume":"185","author":"C Wang","year":"2019","unstructured":"Wang C, Liu Z, Gao H, Fu Y (2019) Vos: A new outlier detection model using virtual graph. Knowl-Based Syst 185:104907. https:\/\/doi.org\/10.1016\/j.knosys.2019.104907","journal-title":"Knowl-Based Syst"},{"key":"4593_CR42","doi-asserted-by":"publisher","first-page":"105331","DOI":"10.1016\/j.knosys.2019.105331","volume":"192","author":"J Xie","year":"2020","unstructured":"Xie J, Xiong Z, Dai Q, Wang X, Zhang Y (2020) A local-gravitation-based method for the detection of outliers and boundary points. Knowl-Based Syst 192:105331. https:\/\/doi.org\/10.1016\/j.knosys.2019.105331","journal-title":"Knowl-Based Syst"},{"key":"4593_CR43","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.ins.2015.06.030","volume":"324","author":"J Ha","year":"2015","unstructured":"Ha J, Seok S, Lee JS (2015) A precise ranking method for outlier detection. Inf Sci 324:88\u2013107. https:\/\/doi.org\/10.1016\/j.ins.2015.06.030","journal-title":"Inf Sci"},{"key":"4593_CR44","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.dss.2014.08.006","volume":"67","author":"HT Pai","year":"2014","unstructured":"Pai HT, Wu F, Hsueh PYSS (2014) A relative patterns discovery for enhancing outlier detection in categorical data. Decis Support Syst 67:90\u201399. https:\/\/doi.org\/10.1016\/j.dss.2014.08.006","journal-title":"Decis Support Syst"},{"key":"4593_CR45","unstructured":"Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04593-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04593-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04593-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T13:17:26Z","timestamp":1697635046000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04593-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":45,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4593"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04593-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,6,18]]},"assertion":[{"value":"26 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author(s) declare no potential conflicts of interest with respect to the research, authorship and\/or publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}