{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:49:15Z","timestamp":1769208555839,"version":"3.49.0"},"reference-count":29,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2009,1,1]],"date-time":"2009-01-01T00:00:00Z","timestamp":1230768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000145","name":"Division of Information and Intelligent Systems","doi-asserted-by":"publisher","award":["IIS-0447814"],"award-info":[{"award-number":["IIS-0447814"]}],"id":[{"id":"10.13039\/100000145","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":[[2009,1]]},"abstract":"<jats:p>\n            Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this article, we address the problem of combining multiple\n            <jats:italic>weighted clusters<\/jats:italic>\n            that belong to different subspaces of the input space. We leverage the diversity of the input clusterings in order to generate a consensus partition that is superior to the participating ones. Since we are dealing with weighted clusters, our consensus functions make use of the weight vectors associated with the clusters. We demonstrate the effectiveness of our techniques by running experiments with several real datasets, including high-dimensional text data. Furthermore, we investigate in depth the issue of diversity and accuracy for our ensemble methods. Our analysis and experimental results show that the proposed techniques are capable of producing a partition that is as good as or better than the best individual clustering.\n          <\/jats:p>","DOI":"10.1145\/1460797.1460800","type":"journal-article","created":{"date-parts":[[2009,1,13]],"date-time":"2009-01-13T13:15:48Z","timestamp":1231852548000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":139,"title":["Weighted cluster ensembles"],"prefix":"10.1145","volume":"2","author":[{"given":"Carlotta","family":"Domeniconi","sequence":"first","affiliation":[{"name":"George Mason University, Fairfax, VA"}]},{"given":"Muna","family":"Al-Razgan","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, VA"}]}],"member":"320","published-online":{"date-parts":[[2009,1,16]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining. 258--269","author":"Al-Razgan M.","unstructured":"Al-Razgan , M. and Domeniconi , C . 2006. Weighted clustering ensembles . In Proceedings of the SIAM International Conference on Data Mining. 258--269 . Al-Razgan, M. and Domeniconi, C. 2006. Weighted clustering ensembles. In Proceedings of the SIAM International Conference on Data Mining. 258--269."},{"key":"e_1_2_1_2_1","unstructured":"Asuncion A. and Newman D. 2007. UCI Machine Learning Repository. http:\/\/www.ics.uci.edu\/~mlearn\/MLR\/epository.html.  Asuncion A. and Newman D. 2007. UCI Machine Learning Repository. http:\/\/www.ics.uci.edu\/~mlearn\/MLR\/epository.html."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the International Workshop on Multiple Classifier Systems. 166--175","author":"Ayad H.","unstructured":"Ayad , H. and Kamel , M . 2003. Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors . In Proceedings of the International Workshop on Multiple Classifier Systems. 166--175 . Ayad, H. and Kamel, M. 2003. Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors. In Proceedings of the International Workshop on Multiple Classifier Systems. 166--175."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502550"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-006-0060-8"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining. 517--520","author":"Domeniconi C.","unstructured":"Domeniconi , C. , Papadopoulos , D. , Gunopulos , D. , and Ma , S . 2004. Subspace clustering of high-dimensional data . In Proceedings of the SIAM International Conference on Data Mining. 517--520 . Domeniconi, C., Papadopoulos, D., Gunopulos, D., and Ma, S. 2004. Subspace clustering of high-dimensional data. In Proceedings of the SIAM International Conference on Data Mining. 517--520."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg038"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the International Conference on Machine Learning. 63--74","author":"Fern X.","unstructured":"Fern , X. and Brodley , C . 2003. Random projection for high-dimensional data clustering: A cluster ensemble approach . In Proceedings of the International Conference on Machine Learning. 63--74 . Fern, X. and Brodley, C. 2003. Random projection for high-dimensional data clustering: A cluster ensemble approach. In Proceedings of the International Conference on Machine Learning. 63--74."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015414"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the International Conference on Pattern Recognition. 276--280","author":"Fred A.","unstructured":"Fred , A. and Jain , A . 2002. Data clustering using evidence accumulation . In Proceedings of the International Conference on Pattern Recognition. 276--280 . Fred, A. and Jain, A. 2002. Data clustering using evidence accumulation. In Proceedings of the International Conference on Pattern Recognition. 276--280."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.113"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081882"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/1009377.1009529"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2005.01.008"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering. 251--258","author":"Hu X.","year":"2004","unstructured":"Hu , X. 2004 . Integration of cluster ensemble and text summarization for gene expression analysis . In Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering. 251--258 . 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On spectral clustering: analysis and an algorithm. In Advances in Neural Information Processing Systems. Vol. 14. 849--856."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007731"},{"key":"e_1_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Punera K. and Ghosh J. 2007. Soft cluster ensembles. In Advances in Fuzzy Clustering and its Applications J. V. de Oliveira and W. Pedrycz Eds. John Wiley &amp; Sons Ltd. 69--90.  Punera K. and Ghosh J. 2007. Soft cluster ensembles. In Advances in Fuzzy Clustering and its Applications J. V. de Oliveira and W. Pedrycz Eds. John Wiley &amp; Sons Ltd. 69--90.","DOI":"10.1002\/9780470061190.ch4"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244303321897735"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the IEEE International Conference on Data Mining. 331--338","author":"Topchy A.","unstructured":"Topchy , A. , Jain , A. , and Punch , W . 2003. Combining multiple weak clusterings . 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