{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:28:31Z","timestamp":1772814511217,"version":"3.50.1"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2020,6,21]],"date-time":"2020-06-21T00:00:00Z","timestamp":1592697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>\n            A huge object collection in high-dimensional space can often be clustered in more than one way, for instance, objects could be clustered by their shape or alternatively by their color. Each grouping represents a different view of the dataset. The new research field of\n            <jats:italic>non-redundant clustering<\/jats:italic>\n            addresses this class of problems. In this article, we follow the approach that different, non-redundant\n            <jats:italic>k<\/jats:italic>\n            -means-like clusterings may exist in different, arbitrarily oriented subspaces of the high-dimensional space. We assume that these subspaces (and optionally a further\n            <jats:italic>noise space<\/jats:italic>\n            without any cluster structure) are orthogonal to each other. This assumption enables a particularly rigorous mathematical treatment of the non-redundant clustering problem and thus a particularly efficient algorithm, which we call N\n            <jats:sc>r<\/jats:sc>\n            -K\n            <jats:sc>means<\/jats:sc>\n            (for non-redundant\n            <jats:italic>k<\/jats:italic>\n            -means). The superiority of our algorithm is demonstrated both theoretically, as well as in extensive experiments. Further, we propose an extension of N\n            <jats:sc>r<\/jats:sc>\n            -K\n            <jats:sc>means<\/jats:sc>\n            that harnesses Hartigan\u2019s dip test to identify the number of clusters for each subspace automatically.\n          <\/jats:p>","DOI":"10.1145\/3385652","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T02:39:58Z","timestamp":1592793598000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans"],"prefix":"10.1145","volume":"14","author":[{"given":"Dominik","family":"Mautz","sequence":"first","affiliation":[{"name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Oettingenstr., M\u00fcnchen, Germany"}]},{"given":"Wei","family":"Ye","sequence":"additional","affiliation":[{"name":"University of California, California, CA"}]},{"given":"Claudia","family":"Plant","sequence":"additional","affiliation":[{"name":"University of Vienna"}]},{"given":"Christian","family":"B\u00f6hm","sequence":"additional","affiliation":[{"name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Oettingenstr., M\u00fcnchen, Germany"}]}],"member":"320","published-online":{"date-parts":[[2020,6,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.128"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, (SODA\u201907)","author":"Arthur David","year":"2007","unstructured":"David Arthur and Sergei Vassilvitskii . 2007 . k-means++: The advantages of careful seeding . In Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, (SODA\u201907) . Nikhil Bansal, Kirk Pruhs, and Clifford Stein (Eds.). SIAM, 1027--1035. http:\/\/dl.acm.org\/citation.cfm?id&equals;1283383.1283494. David Arthur and Sergei Vassilvitskii. 2007. k-means++: The advantages of careful seeding. In Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, (SODA\u201907). Nikhil Bansal, Kirk Pruhs, and Clifford Stein (Eds.). SIAM, 1027--1035. http:\/\/dl.acm.org\/citation.cfm?id&equals;1283383.1283494."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.46"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2006.37"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3200947.3201008"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972832.21"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 7th IEEE International Conference on Data Mining (ICDM\u201907)","author":"Cui Ying","year":"2007","unstructured":"Ying Cui , Xiaoli Z. Fern , and Jennifer G. Dy . 2007. Non-redundant multi-view clustering via orthogonalization . In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM\u201907) . IEEE, 133--142. DOI:https:\/\/doi.org\/10.1109\/ICDM. 2007 .94 10.1109\/ICDM.2007.94 Ying Cui, Xiaoli Z. Fern, and Jennifer G. Dy. 2007. Non-redundant multi-view clustering via orthogonalization. In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM\u201907). IEEE, 133--142. DOI:https:\/\/doi.org\/10.1109\/ICDM.2007.94"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972801.11"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835878"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.141"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcs.96"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.34"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.42"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623734"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339553"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972801.12"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2981345.2981381"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176346577"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.101"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, Part I (Lecture Notes in Computer Science)","author":"Hubig Nina","unstructured":"Nina Hubig and Claudia Plant . 2017. Information-theoretic non-redundant subspace clustering . In Proceedings of the Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, Part I (Lecture Notes in Computer Science) .Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, and Yang-Sae Moon (Eds.), Vol. 10234 . 198--209. DOI:https:\/\/doi.org\/10.1007\/978-3-319-57454-7_16 10.1007\/978-3-319-57454-7_16 Nina Hubig and Claudia Plant. 2017. Information-theoretic non-redundant subspace clustering. In Proceedings of the Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, Part I (Lecture Notes in Computer Science).Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, and Yang-Sae Moon (Eds.), Vol. 10234. 198--209. DOI:https:\/\/doi.org\/10.1007\/978-3-319-57454-7_16"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems","author":"Kalogeratos Argyris","year":"2012","unstructured":"Argyris Kalogeratos and Aristidis Likas . 2012 . Dip-means: An incremental clustering method for estimating the number of clusters . In Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, L\u00e9on Bottou, and Kilian Q. Weinberger (Eds.). 2402--2410. Retrieved from http:\/\/papers.nips.cc\/paper\/4795-dip-means-an-incremental-clustering-method-for-estimating-the-number-of-clusters. Argyris Kalogeratos and Aristidis Likas. 2012. Dip-means: An incremental clustering method for estimating the number of clusters. In Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, L\u00e9on Bottou, and Kilian Q. Weinberger (Eds.). 2402--2410. Retrieved from http:\/\/papers.nips.cc\/paper\/4795-dip-means-an-incremental-clustering-method-for-estimating-the-number-of-clusters."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1497577.1497578"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning.","author":"Kulis Brian","unstructured":"Brian Kulis and Michael I. Jordan . 2012. Revisiting k-means: New algorithms via Bayesian nonparametrics . In Proceedings of the 29th International Coference on International Conference on Machine Learning. Retrieved from http:\/\/icml.cc\/2012\/papers\/291.pdf. Brian Kulis and Michael I. Jordan. 2012. Revisiting k-means: New algorithms via Bayesian nonparametrics. In Proceedings of the 29th International Coference on International Conference on Machine Learning. Retrieved from http:\/\/icml.cc\/2012\/papers\/291.pdf."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2012.2223671"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.9790\/3021-0204719725"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939740"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097989"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219945"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2006.11.013"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401956"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2009.10"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 10th IEEE International Conference on Data Mining. Geoffrey I. Webb, Bing Liu, Chengqi Zhang, Dimitrios Gunopulos, and Xindong Wu (Eds.). IEEE, 521--530","author":"Nguyen Xuan Vinh","year":"2010","unstructured":"Xuan Vinh Nguyen and Julien Epps . 2010 . minCEntropy: A novel information theoretic approach for the generation of alternative clusterings . In Proceedings of the 10th IEEE International Conference on Data Mining. Geoffrey I. Webb, Bing Liu, Chengqi Zhang, Dimitrios Gunopulos, and Xindong Wu (Eds.). IEEE, 521--530 . DOI:https:\/\/doi.org\/10.1109\/ICDM.2010.24 10.1109\/ICDM.2010.24 Xuan Vinh Nguyen and Julien Epps. 2010. minCEntropy: A novel information theoretic approach for the generation of alternative clusterings. In Proceedings of the 10th IEEE International Conference on Data Mining. Geoffrey I. Webb, Bing Liu, Chengqi Zhang, Dimitrios Gunopulos, and Xindong Wu (Eds.). IEEE, 521--530. DOI:https:\/\/doi.org\/10.1109\/ICDM.2010.24"},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the 27th International Conference on Machine Learning (ICML\u201910)","author":"Niu Donglin","year":"2010","unstructured":"Donglin Niu , Jennifer G. Dy , and Michael I. Jordan . 2010. Multiple non-redundant spectral clustering views . In Proceedings of the 27th International Conference on Machine Learning (ICML\u201910) . Johannes F\u00fcrnkranz and Thorsten Joachims (Eds.). Omnipress, 831--838. Retrieved from https:\/\/icml.cc\/Conferences\/ 2010 \/papers\/342.pdf. Donglin Niu, Jennifer G. Dy, and Michael I. Jordan. 2010. Multiple non-redundant spectral clustering views. In Proceedings of the 27th International Conference on Machine Learning (ICML\u201910). Johannes F\u00fcrnkranz and Thorsten Joachims (Eds.). Omnipress, 831--838. Retrieved from https:\/\/icml.cc\/Conferences\/2010\/papers\/342.pdf."},{"key":"e_1_2_1_34_1","volume-title":"Moore","author":"Pelleg Dan","year":"2000","unstructured":"Dan Pelleg and Andrew W . Moore . 2000 . X-means : Extending K-means with efficient estimation of the number of clusters. In Proceedings of the 17th International Conference on Machine Learning (ICML\u201900). Pat Langley (Ed.). Morgan Kaufmann , 727--734. Dan Pelleg and Andrew W. Moore. 2000. X-means: Extending K-means with efficient estimation of the number of clusters. In Proceedings of the 17th International Conference on Machine Learning (ICML\u201900). Pat Langley (Ed.). Morgan Kaufmann, 727--734."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00056"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-012-0258-x"},{"key":"e_1_2_1_37_1","volume-title":"Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Wu Junjie","unstructured":"Junjie Wu , Hui Xiong , and Jian Chen . 2009. Adapting the right measures for K-means clustering . In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . John F. Elder IV, Fran\u00e7oise Fogelman-Souli\u00e9, Peter A. Flach, and Mohammed Javeed Zaki (Eds.). ACM , 877--886. DOI:https:\/\/doi.org\/10.1145\/1557019.1557115 10.1145\/1557019.1557115 Junjie Wu, Hui Xiong, and Jian Chen. 2009. Adapting the right measures for K-means clustering. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. John F. Elder IV, Fran\u00e7oise Fogelman-Souli\u00e9, Peter A. Flach, and Mohammed Javeed Zaki (Eds.). ACM, 877--886. DOI:https:\/\/doi.org\/10.1145\/1557019.1557115"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-016-5601-9"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0068"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3385652","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3385652","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:32:49Z","timestamp":1750199569000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3385652"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,21]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,10,31]]}},"alternative-id":["10.1145\/3385652"],"URL":"https:\/\/doi.org\/10.1145\/3385652","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,21]]},"assertion":[{"value":"2018-12-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-06-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}