{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T08:04:28Z","timestamp":1743667468002,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030320461"},{"type":"electronic","value":"9783030320478"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32047-8_17","type":"book-chapter","created":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T05:07:22Z","timestamp":1569301642000},"page":"188-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MORe++: k-Means Based Outlier Removal on High-Dimensional Data"],"prefix":"10.1007","author":[{"given":"Anna","family":"Beer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer","family":"Lauterbach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Seidl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,23]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Mahmood, A.N.: A novel approach for outlier detection and clustering improvement. In: 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), pp. 577\u2013582. IEEE (2013)","DOI":"10.1109\/ICIEA.2013.6566435"},{"key":"17_CR2","unstructured":"Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027\u20131035. Society for Industrial and Applied Mathematics (2007)"},{"issue":"7","key":"17_CR3","doi-asserted-by":"publisher","first-page":"622","DOI":"10.14778\/2180912.2180915","volume":"5","author":"B Bahmani","year":"2012","unstructured":"Bahmani, B., Moseley, B., Vattani, A., Kumar, R., Vassilvitskii, S.: Scalable k-means++. Proc. VLDB Endow. 5(7), 622\u2013633 (2012)","journal-title":"Proc. VLDB Endow."},{"key":"17_CR4","volume-title":"Adaptive Control Processes: A Guided Tour","author":"RE Bellman","year":"2015","unstructured":"Bellman, R.E.: Adaptive Control Processes: A Guided Tour, vol. 2045. Princeton University Press, Princeton (2015)"},{"key":"17_CR5","unstructured":"Bradley, P.S., Mangasarian, O.L., Street, W.N.: Clustering via concave minimization. In: Advances in Neural Information Processing Systems, pp. 368\u2013374 (1997)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: ACM Sigmod Record, vol. 29, pp. 93\u2013104. ACM (2000)","DOI":"10.1145\/335191.335388"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Chawla, S., Gionis, A.: k-means-: a unified approach to clustering and outlier detection. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 189\u2013197. SIAM (2013)","DOI":"10.1137\/1.9781611972832.21"},{"issue":"8","key":"17_CR8","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recognit. Lett."},{"issue":"4","key":"17_CR9","first-page":"453","volume":"57","author":"D Freedman","year":"1981","unstructured":"Freedman, D., Diaconis, P.: On the histogram as a density estimator: L 2 theory. Probab. Theory Relat. Fields 57(4), 453\u2013476 (1981)","journal-title":"Probab. Theory Relat. Fields"},{"key":"17_CR10","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.patrec.2017.03.008","volume":"90","author":"G Gan","year":"2017","unstructured":"Gan, G., Ng, M.K.P.: K-means clustering with outlier removal. Pattern Recognit. Lett. 90, 8\u201314 (2017)","journal-title":"Pattern Recognit. Lett."},{"key":"17_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/978-3-540-71703-4_17","volume-title":"Advances in Databases: Concepts, Systems and Applications","author":"M Gebski","year":"2007","unstructured":"Gebski, M., Wong, R.K.: An efficient histogram method for outlier detection. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 176\u2013187. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-71703-4_17"},{"key":"17_CR12","unstructured":"Goldstein, M., Dengel, A.: Histogram-based outlier score (HBOS): a fast unsupervised anomaly detection algorithm. In: KI-2012: Poster and Demo Track, pp. 59\u201363 (2012)"},{"key":"17_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1007\/11499145_99","volume-title":"Image Analysis","author":"V Hautam\u00e4ki","year":"2005","unstructured":"Hautam\u00e4ki, V., Cherednichenko, S., K\u00e4rkk\u00e4inen, I., Kinnunen, T., Fr\u00e4nti, P.: Improving K-means by outlier removal. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 978\u2013987. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11499145_99"},{"key":"17_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of Outliers","author":"DM Hawkins","year":"1980","unstructured":"Hawkins, D.M.: Identification of Outliers, vol. 11. Springer, Dordrecht (1980). https:\/\/doi.org\/10.1007\/978-94-015-3994-4"},{"key":"17_CR15","unstructured":"Hyndman, R.J.: The problem with Sturges rule for constructing histograms (1995)"},{"issue":"6\u20137","key":"17_CR16","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/S0167-8655(00)00131-8","volume":"22","author":"MF Jiang","year":"2001","unstructured":"Jiang, M.F., Tseng, S.S., Su, C.M.: Two-phase clustering process for outliers detection. Pattern Recognit. Lett. 22(6\u20137), 691\u2013700 (2001)","journal-title":"Pattern Recognit. Lett."},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Jiang, S., An, Q.: Clustering-based outlier detection method. In: 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 2, pp. 429\u2013433. IEEE (2008)","DOI":"10.1109\/FSKD.2008.244"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Zimek, A., et al.: Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 444\u2013452. ACM (2008)","DOI":"10.1145\/1401890.1401946"},{"issue":"2","key":"17_CR19","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129\u2013137 (1982)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"17_CR20","unstructured":"MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281\u2013297 (1967)"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Mautz, D., Ye, W., Plant, C., B\u00f6hm, C.: Towards an optimal subspace for k-means. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 365\u2013373. ACM (2017)","DOI":"10.1145\/3097983.3097989"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Mautz, D., Ye, W., Plant, C., B\u00f6hm, C.: Discovering non-redundant k-means clusterings in optimal subspaces. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1973\u20131982. ACM (2018)","DOI":"10.1145\/3219819.3219945"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"M\u00fcller, E., Assent, I., Iglesias, P., M\u00fclle, Y., B\u00f6hm, K.: Outlier ranking via subspace analysis in multiple views of the data. In: 2012 IEEE 12th International Conference on Data Mining, pp. 529\u2013538. IEEE (2012)","DOI":"10.1109\/ICDM.2012.112"},{"key":"17_CR24","unstructured":"Papadimitriou, S., Kitagawa, H., Gibbons, P.B., Faloutsos, C.: Loci: fast outlier detection using the local correlation integral. In: Proceedings 19th International Conference on Data Engineering (Cat. No. 03CH37405), pp. 315\u2013326. IEEE (2003)"},{"key":"17_CR25","unstructured":"Rayana, S.: ODDS library (2016). http:\/\/odds.cs.stonybrook.edu"},{"issue":"3","key":"17_CR26","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1093\/biomet\/66.3.605","volume":"66","author":"DW Scott","year":"1979","unstructured":"Scott, D.W.: On optimal and data-based histograms. Biometrika 66(3), 605\u2013610 (1979)","journal-title":"Biometrika"},{"key":"17_CR27","unstructured":"Seidl, T., M\u00fcller, E., Assent, I., Steinhausen, U.: Outlier detection and ranking based on subspace clustering. In: Dagstuhl Seminar Proceedings. Schloss Dagstuhl-Leibniz-Zentrum f\u00fcr Informatik (2009)"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Whang, J.J., Dhillon, I.S., Gleich, D.F.: Non-exhaustive, overlapping k-means. In: Proceedings of the 2015 SIAM International Conference on Data Mining, pp. 936\u2013944. SIAM (2015)","DOI":"10.1137\/1.9781611974010.105"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Yu, H., Cai, X.: A novel k-means algorithm for clustering and outlier detection. In: 2009 Second International Conference on Future Information Technology and Management Engineering, pp. 476\u2013480. IEEE (2009)","DOI":"10.1109\/FITME.2009.125"},{"issue":"5","key":"17_CR30","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11161","volume":"5","author":"A Zimek","year":"2012","unstructured":"Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min.: ASA Data Sci. J. 5(5), 363\u2013387 (2012)","journal-title":"Stat. Anal. Data Min.: ASA Data Sci. J."}],"container-title":["Lecture Notes in Computer Science","Similarity Search and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32047-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T10:45:55Z","timestamp":1710326755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32047-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030320461","9783030320478"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32047-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SISAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Similarity Search and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Newark, NJ","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sisap2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.sisap.org\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.88","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1-92","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}