{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:29:31Z","timestamp":1743125371929,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030902865"},{"type":"electronic","value":"9783030902872"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-90287-2_3","type":"book-chapter","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T16:05:35Z","timestamp":1647273935000},"page":"47-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anomaly Detection Based on Sequence Indexation and CFOF Score Approximation"],"prefix":"10.1007","author":[{"given":"Lucas","family":"Foulon","sequence":"first","affiliation":[]},{"given":"Christophe","family":"Rigotti","sequence":"additional","affiliation":[]},{"given":"Serge","family":"Fenet","sequence":"additional","affiliation":[]},{"given":"Denis","family":"Jouvin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Abr\u00e0moff, M.\u00a0D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.\u00a0C., & Niemeijer, M. (2015). Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology and Visual Science,\u00a057(13), 7.","DOI":"10.1167\/iovs.16-19964"},{"key":"3_CR2","unstructured":"Aggarwal, C. C. (2013). Outlier Analysis. Berlin: Springer Publishing Company, Incorporated."},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Angiulli, F. (2017). Concentration free outlier detection. In Machine Learning and Knowledge Discovery in Databases (pp. 3\u201319). Berlin: Springer International Publishing.","DOI":"10.1007\/978-3-319-71249-9_1"},{"issue":"2","key":"3_CR4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","volume":"29","author":"MM Breunig","year":"2000","unstructured":"Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). Lof: Identifying density-based local outliers. SIGMOD Record,\u00a029(2), 93\u2013104.","journal-title":"SIGMOD Record"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys,\u00a041(3), 15:1\u201315:58.","DOI":"10.1145\/1541880.1541882"},{"issue":"5","key":"3_CR6","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1109\/TKDE.2010.235","volume":"24","author":"V Chandola","year":"2012","unstructured":"Chandola, V., Banerjee, A., & Kumar, V. (2012). Anomaly detection for discrete sequences: A survey. IEEE Transactions on Knowledge and Data Engineering,\u00a024(5), 823\u2013839.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Esling, P., & Agon, C. (2012). Time-series data mining. ACM Computing Surveys,\u00a045(1).","DOI":"10.1145\/2379776.2379788"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Forrest, S., Perelson, A.\u00a0S., Allen, L., & Cherukuri, R. (1994). Self-nonself discrimination in a computer. In Proceedings of the 1994 IEEE Symposium on Security and Privacy (pp. 202\u2013212). IEEE Computer Society.","DOI":"10.1109\/RISP.1994.296580"},{"key":"3_CR9","unstructured":"Knox, E.\u00a0M., & Ng, R.\u00a0T. (1998). Algorithms for mining distance-based outliers in large datasets. In Proceedings of the International Conference on Very Large Data Bases (pp. 392\u2013403)."},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Li, X., Han, J., Kim, S., & Gonzalez, H. (2007). Roam: Rule- and motif-based anomaly detection in massive moving object data sets. In Proceedings of the 2007 SIAM International Conference on Data Mining (pp. 273\u2013284).","DOI":"10.1137\/1.9781611972771.25"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Mukkamala, S., Janoski, G., & Sung, A. (2002). Intrusion detection using neural networks and support vector machines. In Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN\u201902 (Cat. No.02CH37290) (vol.\u00a02, pp. 1702\u20131707).","DOI":"10.1109\/IJCNN.2002.1007774"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Shieh, J., & Keogh, E. (2008). iSAX: Indexing and mining terabyte sized time series. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201908 (pp. 623\u2013631). New York, NY, USA: ACM.","DOI":"10.1145\/1401890.1401966"},{"issue":"1","key":"3_CR13","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s10618-009-0125-6","volume":"19","author":"J Shieh","year":"2009","unstructured":"Shieh, J., & Keogh, E. (2009). iSAX: Disk-aware mining and indexing of massive time series datasets. Data Mining and Knowledge Discovery,\u00a019(1), 24\u201357.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"3_CR14","unstructured":"Ting, K.\u00a0M., Liu, F.\u00a0T., & Zhou, Z. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining(ICDM) (vol.\u00a000, pp. 413\u2013422)."}],"container-title":["Studies in Computational Intelligence","Advances in Knowledge Discovery and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90287-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T16:08:43Z","timestamp":1647274123000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90287-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030902865","9783030902872"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90287-2_3","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}