{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:17:03Z","timestamp":1743038223759,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030864712"},{"type":"electronic","value":"9783030864729"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86472-9_24","type":"book-chapter","created":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T22:02:41Z","timestamp":1630360961000},"page":"261-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Quality of Ensemble Technique for Categorical Data Clustering Using Granule Computing"],"prefix":"10.1007","author":[{"given":"Rahmah","family":"Brnawy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nematollaah","family":"Shiri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"issue":"2","key":"24_CR1","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s10462-018-9642-2","volume":"52","author":"S Abbasi","year":"2018","unstructured":"Abbasi, S., Nejatian, S., Parvin, H., Rezaie, V., Bagherifard, K.: Clustering ensemble selection considering quality and diversity. Artif. Intell. Rev. 52(2), 1311\u20131340 (2018). https:\/\/doi.org\/10.1007\/s10462-018-9642-2","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"24_CR2","doi-asserted-by":"publisher","first-page":"389","DOI":"10.3233\/IDA-140647","volume":"18","author":"H Alizadeh","year":"2014","unstructured":"Alizadeh, H., Minaei-Bidgoli, B., Parvin, H.: Cluster ensemble selection based on a new cluster stability measure. Intell. Data Anal. 18(3), 389\u2013408 (2014)","journal-title":"Intell. Data Anal."},{"key":"24_CR3","unstructured":"Asuncion, A., Newman, D.: UCI machine learning repository (2007)"},{"issue":"5","key":"24_CR4","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1007\/s10489-018-1332-x","volume":"49","author":"A Bagherinia","year":"2018","unstructured":"Bagherinia, A., Minaei-Bidgoli, B., Hossinzadeh, M., Parvin, H.: Elite fuzzy clustering ensemble based on clustering diversity and quality measures. Appl. Intell. 49(5), 1724\u20131747 (2018). https:\/\/doi.org\/10.1007\/s10489-018-1332-x","journal-title":"Appl. Intell."},{"key":"24_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cosrev.2018.01.003","volume":"28","author":"T Boongoen","year":"2018","unstructured":"Boongoen, T., Iam-On, N.: Cluster ensembles: a survey of approaches with recent extensions and applications. Comput. Sci. Rev. 28, 1\u201325 (2018)","journal-title":"Comput. Sci. Rev."},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Brnawy, R., Shiri, N.: K-mixed prototypes: a clustering algorithm for relational data with mixed attribute types. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing (SAC), pp. 542\u2013545 (2019)","DOI":"10.1145\/3297280.3297549"},{"key":"24_CR7","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.fss.2019.07.014","volume":"391","author":"J Chen","year":"2020","unstructured":"Chen, J., Mi, J., Lin, Y.: A graph approach for fuzzy-rough feature selection. Fuzzy Sets Syst. 391, 96\u2013116 (2020)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"24_CR8","first-page":"13","volume":"44","author":"Q Duan","year":"2017","unstructured":"Duan, Q., Yang, Y.L., Li, Y.: Rough k-modes clustering algorithm based on entropy. IAENG Int. J. Comput. Sci. 44(1), 13\u201318 (2017)","journal-title":"IAENG Int. J. Comput. Sci."},{"issue":"1","key":"24_CR9","first-page":"128","volume":"3","author":"XZ Fern","year":"2008","unstructured":"Fern, X.Z., Lin, W.: Cluster ensemble selection. ASA Data Sci. J. 3(1), 128\u2013141 (2008)","journal-title":"ASA Data Sci. J."},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Gan, G., Ma, C., Wu, J.: Data clustering: theory, algorithms, and applications, vol. 20. SIAM (2007)","DOI":"10.1137\/1.9780898718348"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"He, X., Feng, J., Konte, B., Mai, S.T., Plant, C.: Relevant overlapping subspace clusters on categorical data. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213\u2013222 (2014)","DOI":"10.1145\/2623330.2623652"},{"issue":"5","key":"24_CR12","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/BF02948829","volume":"17","author":"Z He","year":"2002","unstructured":"He, Z., Xu, X., Deng, S.: Squeezer: an efficient algorithm for clustering categorical data. J. Comput. Sci. Technol. 17(5), 611\u2013624 (2002)","journal-title":"J. Comput. Sci. Technol."},{"key":"24_CR13","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.knosys.2017.06.020","volume":"132","author":"J Hu","year":"2017","unstructured":"Hu, J., Li, T., Luo, C., Fujita, H., Yang, Y.: Incremental fuzzy cluster ensemble learning based on rough set theory. Knowl.-Based Syst. 132, 144\u2013155 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"24_CR14","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.knosys.2015.10.006","volume":"91","author":"J Hu","year":"2016","unstructured":"Hu, J., Li, T., Wang, H., Fujita, H.: Hierarchical cluster ensemble model based on knowledge granulation. Knowl.-Based Syst. 91, 179\u2013188 (2016)","journal-title":"Knowl.-Based Syst."},{"issue":"5","key":"24_CR15","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/TCYB.2017.2702343","volume":"48","author":"D Huang","year":"2018","unstructured":"Huang, D., Wang, C.D., Lai, J.H.: Locally weighted ensemble clustering. IEEE Trans. Cybern. 48(5), 1460\u20131473 (2018)","journal-title":"IEEE Trans. Cybern."},{"issue":"3","key":"24_CR16","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1109\/TKDE.2010.268","volume":"24","author":"N Iam-On","year":"2012","unstructured":"Iam-On, N., Boongeon, T., Garrett, S., Price, C.: A link-based cluster ensemble approach for categorical data clustering. IEEE Trans. Knowl. Data Eng. 24(3), 413\u2013425 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Jensen, R., Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature new approaches to fuzzy-rough feature selection (2), 1\u201317 (2017)","DOI":"10.1109\/TFUZZ.2008.924209"},{"key":"24_CR18","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.artint.2018.12.007","volume":"273","author":"F Li","year":"2019","unstructured":"Li, F., Qian, Y., Wang, J., Dang, C., Jing, L.: Clustering ensemble based on sample\u2019s stability. Artif. Intell. 273, 37\u201355 (2019)","journal-title":"Artif. Intell."},{"issue":"5","key":"24_CR19","first-page":"1","volume":"12","author":"F Li","year":"2018","unstructured":"Li, F., Qian, Y., Wang, J., Dang, C., Liu, B.: Cluster\u2019s quality evaluation and selective clustering ensemble. ACM Trans. Knowl. Disc. Data (TKDD) 12(5), 1\u201327 (2018)","journal-title":"ACM Trans. Knowl. Disc. Data (TKDD)"},{"issue":"2","key":"24_CR20","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s10115-016-0988-y","volume":"51","author":"Y Ren","year":"2016","unstructured":"Ren, Y., Domeniconi, C., Zhang, G., Yu, G.: Weighted-object ensemble clustering: methods and analysis. Knowl. Inf. Syst. 51(2), 661\u2013689 (2016). https:\/\/doi.org\/10.1007\/s10115-016-0988-y","journal-title":"Knowl. Inf. Syst."},{"key":"24_CR21","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.knosys.2015.01.008","volume":"77","author":"I Saha","year":"2015","unstructured":"Saha, I., Sarkar, J.P., Maulik, U.: Ensemble based rough fuzzy clustering for categorical data. Knowl.-Based Syst. 77, 114\u2013127 (2015)","journal-title":"Knowl.-Based Syst."},{"key":"24_CR22","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.compind.2018.01.014","volume":"97","author":"T Sheeja","year":"2018","unstructured":"Sheeja, T., Kuriakose, A.S.: A novel feature selection method using fuzzy rough sets. Comput. Ind. 97, 111\u2013116 (2018)","journal-title":"Comput. Ind."},{"issue":"03","key":"24_CR23","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1142\/S0218001411008683","volume":"25","author":"S VegaPons","year":"2011","unstructured":"VegaPons, S., RuizShulcloper, J.: A survey of clustering ensemble algorithms. Int. J. Pattern Recognit. Artif. Intell. 25(03), 337\u2013372 (2011)","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"24_CR24","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.knosys.2018.10.038","volume":"164","author":"C Wang","year":"2019","unstructured":"Wang, C., Huang, Y., Shao, M., Fan, X.: Fuzzy rough set-based attribute reduction using distance measures. Knowl.-Based Syst. 164, 205\u2013212 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"24_CR25","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ijar.2018.09.005","volume":"103","author":"Y Yao","year":"2018","unstructured":"Yao, Y.: Three-way decision and granular computing. Int. J. Approx. Reason. 103, 107\u2013123 (2018)","journal-title":"Int. J. Approx. Reason."},{"issue":"7","key":"24_CR26","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1007\/s00500-014-1387-5","volume":"19","author":"J Zhao","year":"2014","unstructured":"Zhao, J., Zhang, Z., Han, C., Zhou, Z.: Complement information entropy for uncertainty measure in fuzzy rough set and its applications. Soft. Comput. 19(7), 1997\u20132010 (2014). https:\/\/doi.org\/10.1007\/s00500-014-1387-5","journal-title":"Soft. Comput."},{"key":"24_CR27","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.patcog.2017.04.019","volume":"69","author":"X Zhao","year":"2017","unstructured":"Zhao, X., Liang, J., Dang, C.: Clustering ensemble selection for categorical data based on internal validity indices. Pattern Recogn. 69, 150\u2013168 (2017)","journal-title":"Pattern Recogn."},{"issue":"8","key":"24_CR28","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1016\/j.patcog.2015.02.014","volume":"48","author":"C Zhong","year":"2015","unstructured":"Zhong, C., Yue, X., Zhang, Z., Lei, J.: A clustering ensemble: two-level-refined co-association matrix with path-based transformation. Pattern Recogn. 48(8), 2699\u20132709 (2015)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86472-9_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:40:34Z","timestamp":1710358834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86472-9_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030864712","9783030864729"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86472-9_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"31 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/dexa2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"149","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"DEXA 2021 Workshops: 50 papers submitted, 23 papers accepted","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}