{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:06:52Z","timestamp":1777604812383,"version":"3.51.4"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s00500-020-05442-1","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T11:03:35Z","timestamp":1606129415000},"page":"4283-4294","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ELOF: fast and memory-efficient anomaly detection algorithm in data streams"],"prefix":"10.1007","volume":"25","author":[{"given":"Yun","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0489-9837","authenticated-orcid":false,"given":"Liang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"ChongJun","family":"Fan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"5442_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal CC (2015) Outlier analysis. In: Data mining. Springer, pp 237\u2013263","DOI":"10.1007\/978-3-319-14142-8_8"},{"key":"5442_CR2","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Han J, Wang J, Yu PS (2004) A framework for projected clustering of high dimensional data streams. In: Proceedings of the thirtieth international conference on Very large data bases, vol 30, pp 852\u2013863","DOI":"10.1016\/B978-012088469-8\/50075-9"},{"issue":"2","key":"5442_CR3","first-page":"310","volume":"28","author":"MB Al-Zoubi","year":"2009","unstructured":"Al-Zoubi MB (2009) An effective clustering-based approach for outlier detection. Eur J Sci Res 28(2):310","journal-title":"Eur J Sci Res"},{"key":"5442_CR4","doi-asserted-by":"crossref","unstructured":"Angiulli F, Fassetti F (2007) Detecting distance-based outliers in streams of data. In: Proceedings of the sixteenth ACM conference on information and knowledge management, pp 811\u2013820","DOI":"10.1145\/1321440.1321552"},{"key":"5442_CR5","doi-asserted-by":"crossref","unstructured":"Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: European conference on principles of data mining and knowledge discovery. Springer, pp 15\u201327","DOI":"10.1007\/3-540-45681-3_2"},{"key":"5442_CR6","doi-asserted-by":"crossref","unstructured":"Assent I, Kranen P, Baldauf C, Seidl T (2012) Anyout: anytime outlier detection on streaming data. In: International conference on database systems for advanced applications. Springer, pp 228\u2013242","DOI":"10.1007\/978-3-642-29038-1_18"},{"key":"5442_CR7","doi-asserted-by":"crossref","unstructured":"Bay SD, Schwabacher M (2003) Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 29\u201338","DOI":"10.1145\/956750.956758"},{"key":"5442_CR8","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp 93\u2013104","DOI":"10.1145\/342009.335388"},{"key":"5442_CR9","doi-asserted-by":"crossref","unstructured":"Cao L, Yang D, Wang Q, Yu Y, Wang J, Rundensteiner EA (IEEE, 2014) Scalable distance-based outlier detection over high-volume data streams. In: 2014 IEEE 30th international conference on data engineering, pp 76\u201387","DOI":"10.1109\/ICDE.2014.6816641"},{"issue":"3","key":"5442_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):1","journal-title":"ACM Comput Surv (CSUR)"},{"key":"5442_CR11","doi-asserted-by":"crossref","unstructured":"Dang TT, Ngan HY, Liu W (, 2015) Distance-based k-nearest neighbors outlier detection method in large-scale traffic data. In: 2015 IEEE international conference on Digital Signal Processing (DSP). IEEE, pp 507\u2013510","DOI":"10.1109\/ICDSP.2015.7251924"},{"issue":"9","key":"5442_CR12","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","volume":"26","author":"M Gupta","year":"2013","unstructured":"Gupta M, Gao J, Aggarwal CC, Han J (2013) Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng 26(9):2250","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5442_CR13","doi-asserted-by":"crossref","unstructured":"Jin W, Tung AK, Han J (2001) Mining top-n local outliers in large databases. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp 293\u2013298","DOI":"10.1145\/502512.502554"},{"key":"5442_CR14","doi-asserted-by":"publisher","first-page":"106186","DOI":"10.1016\/j.knosys.2020.106186","volume":"204","author":"F Liu","year":"2020","unstructured":"Liu F, Yu Y, Song P, Fan Y, Tong X (2020) Scalable KDE-based top-n local outlier detection over large-scale data streams. Knowl Based Syst 204:106186","journal-title":"Knowl Based Syst"},{"issue":"9","key":"5442_CR15","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1002\/wcm.2248","volume":"14","author":"M Moshtaghi","year":"2014","unstructured":"Moshtaghi M, Bezdek JC, Havens TC, Leckie C, Karunasekera S, Rajasegarar S, Palaniswami M (2014) Streaming analysis in wireless sensor networks. Wirel Commun Mobile Comput 14(9):905","journal-title":"Wirel Commun Mobile Comput"},{"key":"5442_CR16","doi-asserted-by":"crossref","unstructured":"Niennattrakul V, Keogh E, Ratanamahatana CA (2010) Data editing techniques to allow the application of distance-based outlier detection to streams. In: 2010 IEEE international conference on data mining. IEEE, pp 947\u2013952","DOI":"10.1109\/ICDM.2010.56"},{"key":"5442_CR17","doi-asserted-by":"crossref","unstructured":"Papadimitriou S, Kitagawa H, Gibbons PB, Faloutsos C (2003) Loci: fast outlier detection using the local correlation integral. In: Proceedings 19th international conference on data engineering (Cat. No. 03CH37405). IEEE, pp 315\u2013326","DOI":"10.1109\/ICDE.2003.1260802"},{"key":"5442_CR18","unstructured":"P\u00f3czos B, Xiong L, Schneider J (2012) Nonparametric divergence estimation with applications to machine learning on distributions. arXiv preprint arXiv:1202.3758"},{"key":"5442_CR19","doi-asserted-by":"crossref","unstructured":"Pokrajac D, Lazarevic A, Latecki LJ (2007) Incremental local outlier detection for data streams. In: 2007 IEEE symposium on computational intelligence and data mining. IEEE, pp 504\u2013515","DOI":"10.1109\/CIDM.2007.368917"},{"issue":"4","key":"5442_CR20","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MWC.2008.4599219","volume":"15","author":"S Rajasegarar","year":"2008","unstructured":"Rajasegarar S, Leckie C, Palaniswami M (2008) Anomaly detection in wireless sensor networks. IEEE Wirel Commun 15(4):34","journal-title":"IEEE Wirel Commun"},{"key":"5442_CR21","doi-asserted-by":"crossref","unstructured":"Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp 427\u2013438","DOI":"10.1145\/342009.335437"},{"issue":"1","key":"5442_CR22","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1145\/2594473.2594479","volume":"15","author":"S Sadik","year":"2014","unstructured":"Sadik S, Gruenwald L (2014) Research issues in outlier detection for data streams. ACM SIGKDD Explor Newsl 15(1):33","journal-title":"ACM SIGKDD Explor Newsl"},{"issue":"12","key":"5442_CR23","doi-asserted-by":"publisher","first-page":"3246","DOI":"10.1109\/TKDE.2016.2597833","volume":"28","author":"M Salehi","year":"2016","unstructured":"Salehi M, Leckie C, Bezdek JC, Vaithianathan T, Zhang X (2016) Fast memory efficient local outlier detection in data streams. IEEE Trans Knowl Data Eng 28(12):3246","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"5442_CR24","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/s10618-012-0300-z","volume":"28","author":"E Schubert","year":"2014","unstructured":"Schubert E, Zimek A, Kriegel HP (2014) Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min Knowl Discov 28(1):190","journal-title":"Data Min Knowl Discov"},{"key":"5442_CR25","doi-asserted-by":"crossref","unstructured":"Sun P, Chawla S (2004) On local spatial outliers. In: Fourth IEEE International Conference on Data Mining (ICDM\u201904). IEEE, pp 209\u2013216","DOI":"10.1109\/ICDM.2004.10097"},{"key":"5442_CR26","doi-asserted-by":"publisher","first-page":"107964","DOI":"10.1109\/ACCESS.2019.2932769","volume":"7","author":"H Wang","year":"2019","unstructured":"Wang H, Bah MJ, Hammad M (2019) Progress in outlier detection techniques: a survey. IEEE Access 7:107964","journal-title":"IEEE Access"},{"key":"5442_CR27","doi-asserted-by":"crossref","unstructured":"Yan Y, Cao L, Kulhman C, Rundensteiner E (2017a) Distributed local outlier detection in big data. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1225\u20131234","DOI":"10.1145\/3097983.3098179"},{"key":"5442_CR28","doi-asserted-by":"crossref","unstructured":"Yan Y, Cao L, Rundensteiner EA (2017b) Scalable top-n local outlier detection. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235\u20131244","DOI":"10.1145\/3097983.3098191"},{"key":"5442_CR29","doi-asserted-by":"crossref","unstructured":"Yang D, Rundensteiner EA, Ward MO (2009) Neighbor-based pattern detection for windows over streaming data. In: Proceedings of the 12th international conference on extending database technology: advances in database technology, pp 529\u2013540","DOI":"10.1145\/1516360.1516422"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05442-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-020-05442-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05442-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T15:12:48Z","timestamp":1730128368000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-020-05442-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["5442"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-05442-1","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,23]]},"assertion":[{"value":"23 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Author Yun Yang declares that she has no conflict of interest. Author Liang Chen declares that he has no conflict of interest. Author ChongJun Fan declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}