{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:25:28Z","timestamp":1742912728003,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030337773"},{"type":"electronic","value":"9783030337780"}],"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\/978-3-030-33778-0_6","type":"book-chapter","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T14:28:50Z","timestamp":1571408930000},"page":"61-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Sampling-Based Approach for Discovering Subspace Clusters"],"prefix":"10.1007","author":[{"given":"Sandy","family":"Moens","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boris","family":"Cule","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bart","family":"Goethals","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,16]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Moise, G., Sander, J., Ester, M.: P3C: a robust projected clustering algorithm. In: Sixth International Conference on Data Mining (ICDM 2006), pp. 414\u2013425. IEEE (2006)","DOI":"10.1109\/ICDM.2006.123"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Aksehirli, E., Goethals, B., Muller, E., Vreeken, J.: Cartification: a neighborhood preserving transformation for mining high dimensional data. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 937\u2013942. IEEE (2013)","DOI":"10.1109\/ICDM.2013.146"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications, vol. 27, no. 2. ACM (1998)","DOI":"10.1145\/276305.276314"},{"issue":"2","key":"6_CR4","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1145\/304181.304188","volume":"28","author":"Charu C. Aggarwal","year":"1999","unstructured":"Aggarwal, C.C., Wolf, J. L., Yu, P.S., Procopiuc, C., Park, J.S.: Fast algorithms for projected clustering. In: ACM SIGMoD Record, vol. 28, no. 2, pp. 61\u201372. ACM (1999)","journal-title":"ACM SIGMOD Record"},{"issue":"4","key":"6_CR5","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":"6_CR6","doi-asserted-by":"crossref","unstructured":"Moens, S., Goethals, B.: Randomly sampling maximal itemsets. In: KDD Workshop on Interactive Data Exploration and Analytics, pp. 79\u201386. ACM (2013)","DOI":"10.1145\/2501511.2501523"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"G\u00fcnnemann, S., F\u00e4rber, I., M\u00fcller, E., Assent, I., Seidl, T.: External evaluation measures for subspace clustering. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1363\u20131372. ACM (2011)","DOI":"10.1145\/2063576.2063774"},{"key":"6_CR8","unstructured":"Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)"},{"key":"6_CR9","doi-asserted-by":"publisher","first-page":"125","DOI":"10.2307\/2528964","volume":"29","author":"DF Andrews","year":"1972","unstructured":"Andrews, D.F.: Plots of high-dimensional data. Biometrics 29, 125\u2013136 (1972)","journal-title":"Biometrics"},{"key":"6_CR10","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, no. 14, pp. 281\u2013297 (1967)"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Procopiuc, C.M., Jones, M., Agarwal, P.K., Murali, T.: A Monte Carlo algorithm for fast projective clustering. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 418\u2013427. ACM (2002)","DOI":"10.1145\/564691.564739"},{"key":"6_CR12","unstructured":"Yiu, M.L., Mamoulis, N.: Frequent-pattern based iterative projected clustering. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 689\u2013692. IEEE (2003)"},{"key":"6_CR13","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226\u2013231 (1996)"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Kailing, K., Kriegel, H.-P., Kr\u00f6ger, P.: Density-connected subspace clustering for high-dimensional data. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 246\u2013256. SIAM (2004)","DOI":"10.1137\/1.9781611972740.23"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Nguyen, H.V., M\u00fcller, E., Vreeken, J., Keller, F., B\u00f6hm, K.: CMI: an information-theoretic contrast measure for enhancing subspace cluster and outlier detection. In: SIAM International Conference on Data Mining, pp. 198\u2013206. SIAM (2013)","DOI":"10.1137\/1.9781611972832.22"},{"key":"6_CR16","unstructured":"Kriegel, H.-P., Kroger, P., Renz, M., Wurst, S.: A generic framework for efficient subspace clustering of high-dimensional data. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), 8-pp. IEEE (2005)"},{"issue":"1","key":"6_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/TCBB.2004.2","volume":"1","author":"SC Madeira","year":"2004","unstructured":"Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE\/ACM Trans. Comput. Biol. Bioinform. (TCBB) 1(1), 24\u201345 (2004)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform. (TCBB)"}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33778-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:32:14Z","timestamp":1709825534000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33778-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030337773","9783030337780"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33778-0_6","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":"16 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Discovery Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Split","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Croatia","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":"28 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dis2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ds2019.irb.hr\/","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":"63","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":"21","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":"19","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":"33% - 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":"3","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":"4","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)"}}]}}