{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:36:27Z","timestamp":1750307787763,"version":"3.41.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2008,1,1]],"date-time":"2008-01-01T00:00:00Z","timestamp":1199145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100004965","name":"Sixth Framework Programme","doi-asserted-by":"publisher","award":["MEIF-CT-2005-011549","MOIF-CT-2004-509920"],"award-info":[{"award-number":["MEIF-CT-2005-011549","MOIF-CT-2004-509920"]}],"id":[{"id":"10.13039\/501100004965","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2008,1]]},"abstract":"<jats:p>Clustering, as an unsupervised learning process is a challenging problem, especially in cases of high-dimensional datasets. Clustering result quality can benefit from user constraints and objective validity assessment. In this article, we propose a semisupervised framework for learning the weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on: (i) user constraints; and (ii) the quality of intermediate clustering results in terms of their structural properties. The proposed framework uses the clustering algorithm and the validity measure as its parameters. We develop and discuss algorithms for learning and tuning the weights of contributing dimensions and defining the \u201cbest\u201d clustering obtained by satisfying user constraints. Experimental results on benchmark datasets demonstrate the superiority of the proposed approach in terms of improved clustering accuracy.<\/jats:p>","DOI":"10.1145\/1324172.1324176","type":"journal-article","created":{"date-parts":[[2008,2,8]],"date-time":"2008-02-08T15:32:16Z","timestamp":1202484736000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A clustering framework based on subjective and objective validity criteria"],"prefix":"10.1145","volume":"1","author":[{"given":"M.","family":"Halkidi","sequence":"first","affiliation":[{"name":"Athens University of Economics and Business, Athens-Greece"}]},{"given":"D.","family":"Gunopulos","sequence":"additional","affiliation":[{"name":"University of Athens, Athens Greece"}]},{"given":"M.","family":"Vazirgiannis","sequence":"additional","affiliation":[{"name":"INRIA\/FUTURS and Athens University of Economics and Business, Athens-Greece"}]},{"given":"N.","family":"Kumar","sequence":"additional","affiliation":[{"name":"University of California, Riverside, CA"}]},{"given":"C.","family":"Domeniconi","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, VA"}]}],"member":"320","published-online":{"date-parts":[[2008,2,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/304182.304188"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335383"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/276304.276314"},{"volume-title":"Proceedings of the International Conference on Machine Learning.","author":"Anderson B.","key":"e_1_2_1_4_1","unstructured":"Anderson , B. , Moore , A. , and Cohn , D . 2000. A nonparametric approach to noisy and costly optimization . In Proceedings of the International Conference on Machine Learning. Anderson, B., Moore, A., and Cohn, D. 2000. A nonparametric approach to noisy and costly optimization. In Proceedings of the International Conference on Machine Learning."},{"volume-title":"Proceedings of the International Conference on Machine Learning (ICML).","author":"Bar-Hillel A.","key":"e_1_2_1_5_1","unstructured":"Bar-Hillel , A. , Hertz , T. , Shental , N. , and Weinshall , D . 2003. Learning distance function using equivalence relations . In Proceedings of the International Conference on Machine Learning (ICML). Bar-Hillel, A., Hertz, T., Shental, N., and Weinshall, D. 2003. Learning distance function using equivalence relations. In Proceedings of the International Conference on Machine Learning (ICML)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014062"},{"key":"e_1_2_1_7_1","unstructured":"Berry M. and Linoff G. 1996. Data Mining Techniques for Marketing: Sale and Customer Support. John Wiley and Sons.   Berry M. and Linoff G. 1996. Data Mining Techniques for Marketing: Sale and Customer Support. John Wiley and Sons."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015360"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.07.027"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 10--15","author":"Ester M.","key":"e_1_2_1_12_1","unstructured":"Ester , M. , Kriegel , H.-P. , Sender , J. , and Xu , X . 1997. Sensity-Connected sets and their application for trend detection in spatial databases . In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 10--15 . Ester, M., Kriegel, H.-P., Sender, J., and Xu, X. 1997. Sensity-Connected sets and their application for trend detection in spatial databases. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 10--15."},{"key":"e_1_2_1_13_1","unstructured":"Fayyad U. G. Piatesky-Shapiro P. S. and Uthurusamy R. 1996. Advances in Knowledge Discovery and Data Mining. AAI Press.   Fayyad U. G. Piatesky-Shapiro P. S. and Uthurusamy R. 1996. Advances in Knowledge Discovery and Data Mining. AAI Press."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022852608280"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2003.08.002"},{"volume-title":"IEEE Conference on Fuzzy Systems.","author":"Gao J.","key":"e_1_2_1_16_1","unstructured":"Gao , J. , Tan , P.-N. , and Cheng , H . 2005. Semi-Supervised fuzzy clustering with pairwise-constrained competitive agglomeration . In IEEE Conference on Fuzzy Systems. Gao, J., Tan, P.-N., and Cheng, H. 2005. Semi-Supervised fuzzy clustering with pairwise-constrained competitive agglomeration. In IEEE Conference on Fuzzy Systems."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.4"},{"volume-title":"Proceedings of the IEEE Conference on Data Mining (ICDM).","author":"Halkidi M.","key":"e_1_2_1_18_1","unstructured":"Halkidi , M. and Vazirgiannis , M . 2001. Clustering validity assessment: Finding the optimal partitioning of a data set . In Proceedings of the IEEE Conference on Data Mining (ICDM). Halkidi, M. and Vazirgiannis, M. 2001. Clustering validity assessment: Finding the optimal partitioning of a data set. In Proceedings of the IEEE Conference on Data Mining (ICDM)."},{"volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 58--65","author":"Hinneburg A.","key":"e_1_2_1_19_1","unstructured":"Hinneburg , A. and Keim , D . 1998. An efficient approach toclustering in large multimedia databases with noise . In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 58--65 . Hinneburg, A. and Keim, D. 1998. An efficient approach toclustering in large multimedia databases with noise. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 58--65."},{"key":"e_1_2_1_20_1","unstructured":"Hogg R. and Craig A. 1978. Introduction to Mathematical Statistics. Macmillan New York.  Hogg R. and Craig A. 1978. Introduction to Mathematical Statistics. Macmillan New York."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Hubert L. and Arabie P. 1985. Comparing partitions. J. Classif.  Hubert L. and Arabie P. 1985. Comparing partitions. J. Classif.","DOI":"10.1007\/BF01908075"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/11430919_94"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102409"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the Symposium on Math, Statistics and Probability","author":"MacQueen J.","year":"1967","unstructured":"MacQueen , J. 1967 . Some methods for classification and analysis of multivariate observations . In Proceedings of the Symposium on Math, Statistics and Probability , University of California Press, Berkeley, CA, 281--297. MacQueen, J. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the Symposium on Math, Statistics and Probability, University of California Press, Berkeley, CA, 281--297."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007692713085"},{"key":"e_1_2_1_27_1","unstructured":"Press W. H. Teukolsky S. A. Vetterling W. T. and Flannery B. P. 1997. Numerical Recipes in C the Art of Scientific Computing. Cambridge University Press.   Press W. H. Teukolsky S. A. Vetterling W. T. and Flannery B. P. 1997. Numerical Recipes in C the Art of Scientific Computing. Cambridge University Press."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg1037"},{"volume-title":"Proceedings of the Artificial Intelligenece and Applications Conference.","author":"Stein B.","key":"e_1_2_1_29_1","unstructured":"Stein , B. , zu Eissen , S. M. , and Wibrock , F . 2003. On cluster validity and the information need of users . In Proceedings of the Artificial Intelligenece and Applications Conference. Stein, B., zu Eissen, S. M., and Wibrock, F. 2003. On cluster validity and the information need of users. In Proceedings of the Artificial Intelligenece and Applications Conference."},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML).","author":"Wagstaff K.","year":"2000","unstructured":"Wagstaff , K. and Cardie . 2000 . Clustering with instance-level constraints . In Proceedings of the International Conference on Machine Learning (ICML). Wagstaff, K. and Cardie. 2000. Clustering with instance-level constraints. In Proceedings of the International Conference on Machine Learning (ICML)."},{"volume-title":"Proceedings of the International Conference on Machine Learning (ICML). 577--584","author":"Wagstaff K.","key":"e_1_2_1_31_1","unstructured":"Wagstaff , K. , Cardie , C. , Rogers , S. , and Schroedl , S . 2001. Constrained k-means clustering with background knowledge . In Proceedings of the International Conference on Machine Learning (ICML). 577--584 . Wagstaff, K., Cardie, C., Rogers, S., and Schroedl, S. 2001. Constrained k-means clustering with background knowledge. In Proceedings of the International Conference on Machine Learning (ICML). 577--584."},{"volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).","author":"Xing E. P.","key":"e_1_2_1_32_1","unstructured":"Xing , E. P. , Ng , A. Y. , Jordan , M. I. , and Russell , S . 2002. Distance metric learning, with application to clustering with side-information . In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). Xing, E. P., Ng, A. Y., Jordan, M. I., and Russell, S. 2002. Distance metric learning, with application to clustering with side-information. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS)."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.96"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1324172.1324176","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1324172.1324176","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T13:56:15Z","timestamp":1750254975000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1324172.1324176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,1]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2008,1]]}},"alternative-id":["10.1145\/1324172.1324176"],"URL":"https:\/\/doi.org\/10.1145\/1324172.1324176","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2008,1]]},"assertion":[{"value":"2006-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2007-08-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2008-02-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}