{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:12:58Z","timestamp":1767676378716,"version":"3.41.0"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319534794"},{"type":"electronic","value":"9783319534800"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-53480-0_37","type":"book-chapter","created":{"date-parts":[[2017,2,22]],"date-time":"2017-02-22T10:12:19Z","timestamp":1487758339000},"page":"372-383","source":"Crossref","is-referenced-by-count":15,"title":["A Survey on Outlier Detection in the Context of Stream Mining: Review of Existing Approaches and Recommadations"],"prefix":"10.1007","author":[{"given":"Imen","family":"Souiden","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaki","family":"Brahmi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hajer","family":"Toumi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,2,23]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-14142-8","volume-title":"Data Mining: The Textbook","author":"CC Aggarwal","year":"2015","unstructured":"Aggarwal, C.C.: Data Mining: The Textbook. Springer, Switzerland (2015)"},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"Karimian, S.H., Kelarestaghi, M., Hashemi, S.: I-inclof: improved incremental local outlier detection for data streams. In: 16th CSI International Symposium on Artificial Intelligence and Signal Processing (2012)","DOI":"10.1109\/AISP.2012.6313711"},{"key":"37_CR3","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1147\/JRD.2011.2163280","volume":"55","author":"MS Beigi","year":"2011","unstructured":"Beigi, M.S., Ebadollahi, S., Chang, S.F., Verma, D.C.: Anomaly detection in information streams without prior domain knowledge. IBM J. Res. Dev. 55, 550\u2013560 (2011)","journal-title":"IBM J. Res. Dev."},{"key":"37_CR4","first-page":"13","volume":"136","author":"P Thakkar","year":"2016","unstructured":"Thakkar, P., Vala, J., Prajapati, V.: Survey on outlier detection in data stream. Int. J. Comput. Appl. 136, 13\u201316 (2016)","journal-title":"Int. J. Comput. Appl."},{"key":"37_CR5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/2594473.2594479","volume":"15","author":"MS Sadik","year":"2014","unstructured":"Sadik, M.S., Gruenwald, L.: Research issues in outlier detection for data streams. ACM SIGKDD Explor. 15, 33\u201340 (2014)","journal-title":"ACM SIGKDD Explor."},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Stevanovic, D., Vlajic, N.: Next generation application-layer DDoS defences: applying the concepts of outlier detection in data streams with concept drift. In: 2014 13th International Conference on Machine Learning and Applications (2014)","DOI":"10.1109\/ICMLA.2014.80"},{"key":"37_CR7","first-page":"158","volume":"2","author":"Z Miller","year":"2011","unstructured":"Miller, Z., Deitrick, W., Hu, W.: Anomalous network packet detection using data stream mining. J. Inf. Secur. 2, 158\u2013168 (2011)","journal-title":"J. Inf. Secur."},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J., Rundensteiner, E.A.: Scalable distance-based outlier detection over high-volume data streams. In: 30th International Conference on Data Engineering (2014)","DOI":"10.1109\/ICDE.2014.6816641"},{"issue":"2","key":"37_CR9","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1007\/s10618-009-0159-9","volume":"20","author":"F Angiulli","year":"2010","unstructured":"Angiulli, F., Fassetti, F.: Distance-based outlier queries in data streams: the novel task and algorithms. Data Min. Knowl. Disc. 20(2), 290\u2013324 (2010)","journal-title":"Data Min. Knowl. Disc."},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Yang, D., Rundensteiner, E., Ward, M.: Neighbor-based pattern detection for windows over streaming data. In: EDBT 2009, pp. 529\u2013540 (2009)","DOI":"10.1145\/1516360.1516422"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Continuous monitoring of distance-based outliers over data streams. In: The 27th International Conference on Data Engineering (ICDE), pp. 135\u2013146 (2011)","DOI":"10.1109\/ICDE.2011.5767923"},{"issue":"C","key":"37_CR12","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.is.2015.07.006","volume":"55","author":"M Kontaki","year":"2016","unstructured":"Kontaki, M., Gounarisn, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Effcient and flexible algorithms for monitoring distance based outliers over data streams. Inf. Syst. 55(C), 37\u201353 (2016)","journal-title":"Inf. Syst."},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density based local outliers. In: 2000 ACM SIGMOD International Conference on Management of Data, vol. 29(2), pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Pokrajac, D., Lazarevic, A., Latecki, L.J.: Incremental local outlier detection for data streams. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 504\u2013515 (2007)","DOI":"10.1109\/CIDM.2007.368917"},{"key":"37_CR15","unstructured":"Christopher, T., Divya, M.T.: A comparative analysis of hierarchical and partitioning clustering algorithms for outlier detection in data streams. Int. J. Adv. Res. Comput. Commun. Eng. (2015)"},{"key":"37_CR16","unstructured":"Mathur, N., Tiwari, M., Khandelwal, S.: Increased performance factor for the best clustering algorithm. Int. J. Eng. Tech. Res. 3 (2015)"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Yogita, T.D.: A framework for outlier detection in evolving data streams by weighting attributes in clustering. In: 2nd International Conference on Communication Computing and Security (2012)","DOI":"10.1016\/j.protcy.2012.10.026"},{"key":"37_CR18","first-page":"25","volume":"2","author":"HM Koupaie","year":"2013","unstructured":"Koupaie, H.M., Ibrahim, S., Hosseinkhani, J.: Outlier detection in stream data by clustering method. Int. J. Adv. Comput. Sci. Inf. Technol. 2, 25\u201334 (2013)","journal-title":"Int. J. Adv. Comput. Sci. Inf. Technol."},{"key":"37_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/978-3-642-29038-1_18","volume-title":"Database Systems for Advanced Applications","author":"I Assent","year":"2012","unstructured":"Assent, I., Kranen, P., Baldauf, C., Seidl, T.: AnyOut: anytime outlier detection on streaming data. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 228\u2013242. Springer, Heidelberg (2012). doi: 10.1007\/978-3-642-29038-1_18"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: 2006 SIAM Conference on Data Mining, pp. 328\u2013339 (2006)","DOI":"10.1137\/1.9781611972764.29"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Li-xiong, L., Jing, K., Yun-fei, G., Hai, H.: A three-step clustering algorithm over an evolving data stream. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 160\u2013164 (2009)","DOI":"10.1109\/ICICISYS.2009.5357749"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Kumar, M., Sharma, A.: Mining of data stream using DDenStream clustering algorithm. In: 2013 International Conference in MOOC on Data Engineering, pp. 315\u2013320 (2013)","DOI":"10.1109\/MITE.2013.6756357"},{"key":"37_CR23","unstructured":"Yogita, T.D.: Unsupervised outlier detection in streaming data using weighted clustering. In: 12th International Conference on Intelligent Systems Design and Applications, pp. 160\u2013164 (2012)"},{"key":"37_CR24","unstructured":"Gurav, R.B., Rangdale, S.: Hybrid approach for outlier detection in high dimensional dataset. Int. J. Sci. Res. 3 (2014)"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Solaimani, M., Iftekhar, M., Khan, L.: Statistical technique for online anomaly detection using spark over heterogeneous data from multi-source VMware performance data. In: IEEE International Conference on Big Data (2014)","DOI":"10.1109\/BigData.2014.7004343"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Kumar Samparthi, V.S., Verma, H.K.: Outlier detection of data in wireless sensor networks using kernel density estimation. Int. J. Comput. Appl. 5 (2010)","DOI":"10.5120\/924-1302"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Uddin, M.S., Kuh, A., Weng, Y., Ili\u2019c, M.: Online bad data detection using kernel density estimation. In: IEEE Power and Energy Sociaty and General Meeting (2015)","DOI":"10.1109\/PESGM.2015.7286013"},{"key":"37_CR28","doi-asserted-by":"crossref","unstructured":"Tang, X., Li, G., Chen, G.: Fast detecting outliers over online data streams. In: International Conference on Information Engineering and Computer Science, pp. 1\u20134 (2009)","DOI":"10.1109\/ICIECS.2009.5363123"},{"key":"37_CR29","first-page":"66","volume":"5","author":"F Lin","year":"2010","unstructured":"Lin, F., Le, W., Bo, J.: Research on maximal frequent pattern outlier factor for online high dimensional time-series outlier detection. J. Convergence Inf. Technol. 5, 66\u201371 (2010)","journal-title":"J. Convergence Inf. Technol."},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Dominic, D.D., Said, A.M.: Network anomaly detection approach based on frequent pattern mining technique. In: International Conference on Computational Science and Technology (2014)","DOI":"10.1109\/ICCST.2014.7045011"},{"key":"37_CR31","first-page":"55","volume":"20","author":"AM Said","year":"2015","unstructured":"Said, A.M., Dominic, P.D.D., Faye, L.: Data stream outlier detection approach based on frequent pattern mining technique. Int. J. Bus. Inf. Syst. 20, 55\u201370 (2015)","journal-title":"Int. J. Bus. Inf. Syst."},{"key":"37_CR32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/SURV.2010.021510.00088","volume":"12","author":"Y Zhang","year":"2010","unstructured":"Zhang, Y., Meratnia, N., Havinga, P.: Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun. Surv. Tutorials 12, 159\u2013170 (2010)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"37_CR33","first-page":"1","volume":"110","author":"A Kale","year":"2015","unstructured":"Kale, A., Ingle, M.D.: SVM based feature extraction for novel class detection from streaming data. Wireless Pers. Commun. J. 110, 1\u20133 (2015)","journal-title":"Wireless Pers. Commun. J."},{"key":"37_CR34","doi-asserted-by":"crossref","unstructured":"Masud, M.M., Gao, J., Han, J., Khan, L., Thuraising-ham, B.M.: Classification and adaptive novel class detection of feature-evolving data streams. IEEE Trans. Knowl. Data Eng. 25 (2013)","DOI":"10.1109\/TKDE.2012.109"},{"key":"37_CR35","doi-asserted-by":"crossref","unstructured":"Uddin, M.S., Kuh, A.: Online least-squares one-class support vector machine for outlier detection in power grid data. In: IEEE International Conference on Acoustics Speech and Signal Processing (2016)","DOI":"10.1109\/ICASSP.2016.7472153"},{"key":"37_CR36","doi-asserted-by":"crossref","unstructured":"Ye, H., Kitagawa, H., Xia, J.: Continuous angle-based outlier detection on high-dimensional data streams. In: 19th International Database Engineering and Applications Symposium, pp. 162\u2013167 (2015)","DOI":"10.1145\/2790755.2790775"},{"key":"37_CR37","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Hubert, M.S., Zimek, A.: Angle based outlier detection in high-dimensional data. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)","DOI":"10.1145\/1401890.1401946"},{"key":"37_CR38","unstructured":"Salperwyck, C.: Apprentissage incr\u00e9mental en-ligne sur flux de donn\u00e9es. Ph.D. thesis, University Charles de Gaulle (2012)"},{"key":"37_CR39","unstructured":"Marascu, A.: Extraction de motifs s\u00e9quentiels dans les flux de donn\u00e9es. Ph.D. thesis, Universit\u00e9 de Nice Sophia Antipolis (2009)"},{"key":"37_CR40","doi-asserted-by":"crossref","unstructured":"Salehi, M., Leckie, C., Bezdek, J., Vaithianathan, T.: Local outlier detection for data streams in sensor networks: revisiting the utility problem invited paper. In: 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2015)","DOI":"10.1109\/ISSNIP.2015.7106978"}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Systems Design and Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-53480-0_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T15:37:35Z","timestamp":1750001855000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-53480-0_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319534794","9783319534800"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-53480-0_37","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2017]]}}}