{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T13:07:47Z","timestamp":1648904867255},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2010,3,11]],"date-time":"2010-03-11T00:00:00Z","timestamp":1268265600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2011,6]]},"DOI":"10.1007\/s00500-010-0575-1","type":"journal-article","created":{"date-parts":[[2010,3,10]],"date-time":"2010-03-10T05:20:28Z","timestamp":1268198428000},"page":"1195-1215","source":"Crossref","is-referenced-by-count":14,"title":["Detecting anomalies from high-dimensional wireless network data streams: a case study"],"prefix":"10.1007","volume":"15","author":[{"given":"Ji","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Qigang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Hai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,3,11]]},"reference":[{"key":"575_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal CC (2005) On abnormality detection in spuriously populated data streams. In: 2005 SIAM international conference on data mining (SDM\u201905), Newport Beach, pp 84\u201393","DOI":"10.1137\/1.9781611972757.8"},{"key":"575_CR2","unstructured":"Aggarwal CC, Yu PS (2001) Outlier detection in high dimensional data. In: 2001 ACM SIGMOD international conference on management of data (SIGMOD\u201901). Santa Barbara, pp 37\u201346"},{"key":"575_CR3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00778-004-0125-5","volume":"14","author":"CC Aggarwal","year":"2005","unstructured":"Aggarwal CC, Yu PS (2005) An effective and efficient algorithm for high-dimensional outlier detection. VLDB J 14:211\u2013221","journal-title":"VLDB J"},{"key":"575_CR4","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: 2003 very large database conference (VLDB\u201903), Berlin, Germany, pp 81\u201392","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"575_CR5","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: 2004 Very large database conference (VLDB\u201904), Toronto, Canada, pp 852\u2013863","DOI":"10.1016\/B978-012088469-8.50075-9"},{"key":"575_CR6","doi-asserted-by":"crossref","unstructured":"Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: 2002 European conference on principles of data mining and knowledge discovery (PKDD\u201902). Helsinki, Finland, pp 15\u201326","DOI":"10.1007\/3-540-45681-3_2"},{"key":"575_CR7","doi-asserted-by":"crossref","unstructured":"Barbara D (2002) Requirements for clustering data streams. ACM SIGKDD Explorations Newsletter, vol 3, Issue 2. ACM Press, London, pp 23\u201327","DOI":"10.1145\/507515.507519"},{"key":"575_CR8","doi-asserted-by":"crossref","unstructured":"Balazinska M, Castro P (2003) Characterizing mobility and network usage in a corporate wireless local-area network. In: 2003 International conference on mobile systems, applications, and services (MobiSys\u201903), San Francisco, CA, USA, pp 232\u2013239","DOI":"10.1145\/1066116.1066127"},{"key":"575_CR9","doi-asserted-by":"crossref","unstructured":"Breuning M, Kriegel HP, Ng R, Sander J (2000) LOF: identifying density-based local outliers. In: 2000 ACM SIGMOD international conference on management of data (SIGMOD\u201900), Dallas, Texas, USA, pp 93\u2013104","DOI":"10.1145\/342009.335388"},{"key":"575_CR10","doi-asserted-by":"crossref","unstructured":"Boudjeloud L, Poulet F (2005) Visual interactive evolutionary algorithm for high dimensional data clustering and outlier detection. In: 9th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD\u201905). Hanoi, Vietnam, pp 426\u2013431","DOI":"10.1007\/11430919_50"},{"key":"575_CR11","unstructured":"Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S (2002) A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. In: Applications of Data Mining in Computer Security, pp 34\u201342"},{"key":"575_CR12","doi-asserted-by":"crossref","unstructured":"Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: 1984 ACM SIGMOD international conference on management of data (SIGMOD\u201984). Boston, Massachusetts, pp 47\u201357","DOI":"10.1145\/602259.602266"},{"key":"575_CR13","doi-asserted-by":"crossref","unstructured":"Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. In: 1998 ACM SIGMOD international conference on management of data (SIGMOD\u201998). Seattle, WA, USA, pp 73\u201384","DOI":"10.1145\/276304.276312"},{"key":"575_CR14","unstructured":"Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufman Publishers,"},{"key":"575_CR15","doi-asserted-by":"crossref","unstructured":"Jin W, Tung AKH, Han J, Wang W (2006) Ranking outliers using symmetric neighborhood relationship. 2006 Pacific-Asia conference on knowledge discovery and data mining (PAKDD\u201906), Singapore, pp 577\u2013593","DOI":"10.1007\/11731139_68"},{"key":"575_CR16","unstructured":"Knorr EM, Ng R (1998) Algorithms for mining distance-based outliers in large dataset. In: 1998 Very large database conference (VLDB\u201998). New York, NY, USA, pp 392\u2013403"},{"key":"575_CR17","unstructured":"Knorr EM, Ng R (1999) Finding intentional knowledge of distance-based outliers. In: 1999 Very large database conference (VLDB\u201999), Edinburgh, Scotland, pp 211\u2013222"},{"key":"575_CR18","unstructured":"Khoshgoftaar TM, Nath SV, Zhong S (2005) Intrusion detection in wireless networks using clusterings techniques with expert analysis. In: The fourth international conference on machine leaning and applications (ICMLA\u201905), Los Angeles, CA, USA, pp 54\u201363"},{"key":"575_CR19","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316801","volume-title":"Finding groups in data: an introduction to cluster analysis","author":"L Kaufman","year":"1990","unstructured":"Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York"},{"key":"575_CR20","unstructured":"Nguyen HV, Gopalkrishnan V (2009) Efficient pruning schemes for distance-based outlier detection. In: 2009 European conference on machine learning and knowledge discovery in databases (ECML\/PKDD\u201909), Bled, Slovinia, pp 160\u2013175"},{"key":"575_CR21","doi-asserted-by":"crossref","unstructured":"Pokrajac D, Lazarevic A, Latecki L (2007) Incremental local outlier detection for data streams. In: IEEE symposiums on computational intelligence and data mining (CIDM\u201907). Honolulu, Hawaii, USA, pp 504\u2013515","DOI":"10.1109\/CIDM.2007.368917"},{"issue":"4","key":"575_CR22","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1145\/959060.959074","volume":"32","author":"T Palpanas","year":"2003","unstructured":"Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2003) Distributed deviation detection in sensor networks. SIGMOD Record 32(4):77\u201382","journal-title":"SIGMOD Record"},{"key":"575_CR23","doi-asserted-by":"crossref","unstructured":"Ramaswamy S, Rastogi R, Kyuseok S (2000) efficient algorithms for mining outliers from large data sets. In: 2000 ACM SIGMOD international conference on management of data (SIGMOD\u201900). Dallas Texas, USA, pp 427\u2013438","DOI":"10.1145\/342009.335437"},{"key":"575_CR24","doi-asserted-by":"crossref","unstructured":"Tang J, Chen Z, Fu A, Cheung DW (2002) Enhancing effectiveness of outlier detections for low density patterns. In: 2002 Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD\u201902), Taipei, Taiwan, pp 535\u2013548","DOI":"10.1007\/3-540-47887-6_53"},{"key":"575_CR25","doi-asserted-by":"crossref","unstructured":"Wang W, Zhang J, Wang H (2005) Grid-ODF: detecting outliers effectively and efficiently in large multi-dimensional databases. In: 2005 International conference on computational intelligence and security (CIS\u201905), Xi\u2019an, China, pp 765\u2013770","DOI":"10.1007\/11596448_113"},{"key":"575_CR26","doi-asserted-by":"crossref","unstructured":"Wang B, Xiao G, Yu H, Yang X (2009) Distance-based outlier detection on uncertain data. In: 2009 Ninth IEEE international conference on computer and information technology, Xiamen, China, pp 293\u2013298","DOI":"10.1109\/CIT.2009.107"},{"key":"575_CR27","unstructured":"Zhu C, Kitagawa H, Faloutsos C (2005) Example-based robust outlier detection in high dimensional datasets. In: 2005 IEEE international conference on data mining (ICDM\u201905), Houston, Texas, USA, pp 829\u2013832"},{"key":"575_CR28","doi-asserted-by":"crossref","unstructured":"Zhu C, Kitagawa H, Papadimitriou S, Faloutsos C (2004) OBE: outlier by example. In: 2004 Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD\u201904), Sydney, Australia, pp 222\u2013234","DOI":"10.1007\/978-3-540-24775-3_29"},{"key":"575_CR29","unstructured":"Zhang J, Lou M, Ling TW, Wang H (2004) HOS-Miner: a system for detecting outlying subspaces of high-dimensional data. In: 2004 Very large database conference (VLDB\u201904), Toronto, Canada, pp 1265\u20131268"},{"key":"575_CR30","doi-asserted-by":"crossref","unstructured":"Zhang J, Wang H (2006) Detecting outlying subspaces for high-dimensional data: the new task, algorithms and performance. In: Knowledge and information systems (KAIS), pp 333\u2013355","DOI":"10.1007\/s10115-006-0020-z"},{"key":"575_CR31","doi-asserted-by":"crossref","unstructured":"Zhang J, Gao Q, Wang H (2006) A novel method for detecting outlying subspaces in high-dimensional databases using genetic algorithm. In: 2006 International conference on data mining (ICDM\u201906), Hong Kong, China, pp 731\u2013740","DOI":"10.1109\/ICDM.2006.6"},{"key":"575_CR32","doi-asserted-by":"crossref","unstructured":"Zhong C, Lin X, Zhang M (2009) A local outlier detection approach based on graph-cut. In: 2009 International joint conference on computational sciences and optimization, Sanya, China, pp 714\u2013718","DOI":"10.1109\/CSO.2009.272"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-010-0575-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-010-0575-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-010-0575-1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T01:40:01Z","timestamp":1559094001000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-010-0575-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,3,11]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2011,6]]}},"alternative-id":["575"],"URL":"https:\/\/doi.org\/10.1007\/s00500-010-0575-1","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,3,11]]}}}