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Knowl. Discov. Data"],"published-print":{"date-parts":[[2014,11,17]]},"abstract":"<jats:p>Ensemble clustering, as an important extension of the clustering problem, refers to the problem of combining different (input) clusterings of a given dataset to generate a final (consensus) clustering that is a better fit in some sense than existing clusterings. Over the past few years, many ensemble clustering approaches have been developed. However, most of them are designed for partitional clustering methods, and few research efforts have been reported for ensemble hierarchical clustering methods. In this article, a hierarchical ensemble clustering framework that can naturally combine both partitional clustering and hierarchical clustering results is proposed. In addition, a novel method for learning the ultra-metric distance from the aggregated distance matrices and generating final hierarchical clustering with enhanced cluster separation is developed based on the ultra-metric distance for hierarchical clustering. We study three important problems: dendrogram description, dendrogram combination, and dendrogram selection. We develop two approaches for dendrogram selection based on tree distances, and we investigate various dendrogram distances for representing dendrograms. We provide a systematic empirical study of the ensemble hierarchical clustering problem. Experimental results demonstrate the effectiveness of our proposed approaches.<\/jats:p>","DOI":"10.1145\/2611380","type":"journal-article","created":{"date-parts":[[2014,10,1]],"date-time":"2014-10-01T13:34:59Z","timestamp":1412170499000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["A Framework for Hierarchical Ensemble Clustering"],"prefix":"10.1145","volume":"9","author":[{"given":"Li","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China and School of Computer Science, Florida International University, Miami, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Florida International University, Miami, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Texas at Arlington, Arlington, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,9,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01894192"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.2307\/2412432"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539795296334"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.2005.36"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI\u201909)","author":"Azimi J.","unstructured":"J. Azimi and X. Fern . 2009. Adaptive cluster ensemble selection . In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI\u201909) . 992--997. J. Azimi and X. Fern. 2009. Adaptive cluster ensemble selection. 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Combining hierarchical clusterings using min-transitive closure . In Proceedings of the 19th International Conference on Pattern Recognition (ICPR\u201908) . IEEE, 1--4. A. Mirzaei and M. Rahmati. 2008. Combining hierarchical clusterings using min-transitive closure. In Proceedings of the 19th International Conference on Pattern Recognition (ICPR\u201908). IEEE, 1--4."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2009.2034531"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/1497136.1497139"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023949509487"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s003570000007"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 2011 19th Iranian Conference on Electrical Engineering (ICEE\u201911)","author":"Rashedi E.","unstructured":"E. Rashedi and A. Mirzaei . 2011. A novel multi-clustering method for hierarchical clusterings based on boosting . 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