{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:24:22Z","timestamp":1760243062979,"version":"build-2065373602"},"reference-count":13,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,6,26]],"date-time":"2015-06-26T00:00:00Z","timestamp":1435276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>CLOPE (Clustering with sLOPE) is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a different sequence. In this paper, a hierarchical clustering framework is proposed as an extension of CLOPE to generate stable and satisfactory clustering results based on an optimized agglomerative merge process. The new clustering profit is defined as the merge criteria and the cluster graph structure is proposed to optimize the merge iteration process. The experiments conducted on two datasets both demonstrate that the agglomerative approach achieves stable clustering results with a better profit value, but costs much more time due to the worse complexity.<\/jats:p>","DOI":"10.3390\/a8030380","type":"journal-article","created":{"date-parts":[[2015,6,26]],"date-time":"2015-06-26T10:24:46Z","timestamp":1435314286000},"page":"380-394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving CLOPE\u2019s Profit Value and Stability with an Optimized Agglomerative Approach"],"prefix":"10.3390","volume":"8","author":[{"given":"Yefeng","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Donghua University, Shanghai 201620, China"},{"name":"School of Computer Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajin","family":"Le","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.inffus.2004.03.001","article-title":"A cluster ensemble method for clustering categorical data","volume":"6","author":"He","year":"2005","journal-title":"Inf. Fusion"},{"key":"ref_2","unstructured":"Gibson, D., Kleiberg, J., and Raghavan, P. (1998, January 24\u201327). Clustering categorical data: An approach based on dynamic systems. Proceedings of the VLDB\u201998, New York, NY, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guha, S., Rastogi, R., and Shim, K. (1999, January 23\u201326). A robust clustering algorithm for categorical attributes. Proceedings of the ICDE\u201999, Sydney, Australia.","DOI":"10.1109\/ICDE.1999.754967"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, K., Xu, C., and Liu, B. (1999, January 2\u20136). Clustering transactions using large items. Proceedings of the CIKM\u201999, Kansas City, MI, USA.","DOI":"10.1145\/319950.320054"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/BF02948829","article-title":"Squeezer: An efficient algorithm for clustering categorical data","volume":"17","author":"He","year":"2002","journal-title":"J. Comp. Sci. Tech."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, Y., Guan, S., and You, J. (2002, January 23\u201323). CLOPE: A fast and effective clustering algorithm for transactional data. Proceedings of the KDD\u201902, Edmonton, AB, Canada.","DOI":"10.1145\/775107.775149"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Barbar\u00e1, D., Li, Y., and Couto, J. (2002, January 4\u20139). COOLCAT: An entropy-based algorithm for categorical clustering. 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[2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/8\/3\/380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:48:27Z","timestamp":1760215707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/8\/3\/380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,26]]},"references-count":13,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2015,9]]}},"alternative-id":["a8030380"],"URL":"https:\/\/doi.org\/10.3390\/a8030380","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2015,6,26]]}}}