{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:19:12Z","timestamp":1743157152789,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031783821"},{"type":"electronic","value":"9783031783838"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78383-8_7","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:52:37Z","timestamp":1733089957000},"page":"93-108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Extension of Random Forest-Clustering Schemes Which Works with Partition-Level Constraints"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1008-3917","authenticated-orcid":false,"given":"Manuele","family":"Bicego","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hafiz Ahmad","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"issue":"1","key":"7_CR1","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/s10618-019-00660-0","volume":"34","author":"S Aryal","year":"2020","unstructured":"Aryal, S., Ting, K., Washio, T., Haffari, G.: A comparative study of data-dependent approaches without learning in measuring similarities of data objects. Data Min. Knowl. Disc. 34(1), 124\u2013162 (2020)","journal-title":"Data Min. Knowl. Disc."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Bicego, M., Cicalese, F., Mensi, A.: RatioRF: A novel measure for random forest clustering based on the tversky\u2019s ratio model. IEEE Tr. on Knowledge and Data Engineering 35(1), 830\u2013841 (2023)","DOI":"10.1109\/TKDE.2021.3086147"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Bicego, M., Escolano, F.: On learning random forests for random forest-clustering. In: Proc. Int. Conf. on Pattern Recognition. pp. 3451\u20133458. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412014"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Bicego, M.: K-random forests: a k-means style algorithm for random forest clustering. In: Proc. Int. Joint Conf. on Neural Networks. pp.\u00a01\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8851820"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Bicego, M., Cicalese, F.: On the good behaviour of extremely randomized trees in random forest-distance computation. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 645\u2013660. Springer (2023)","DOI":"10.1007\/978-3-031-43421-1_38"},{"issue":"1","key":"7_CR6","first-page":"830","volume":"35","author":"M Bicego","year":"2023","unstructured":"Bicego, M., Cicalese, F., Mensi, A.: RatioRF: a novel measure for random forest clustering based on the Tversky\u2019s ratio model. IEEE Trans. Knowl. Data Eng. 35(1), 830\u2013841 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"7_CR8","unstructured":"Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth (1984)"},{"issue":"3","key":"7_CR9","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3233\/IDA-130590","volume":"17","author":"TF Covoes","year":"2013","unstructured":"Covoes, T.F., Hruschka, E.R., Ghosh, J.: A study of k-means-based algorithms for constrained clustering. Intelligent Data Analysis 17(3), 485\u2013505 (2013)","journal-title":"Intelligent Data Analysis"},{"key":"7_CR10","unstructured":"Davidson, I., Basu, S.: A survey of clustering with instance level constraints. ACM Trans. on Knowledge Discovery from data 1(1-41), 2\u201342 (2007)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Davidson, I., Ravi, S.: Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. In: Proc. Europ. Conf. on Principles of Data Mining and Knowledge Discovery. pp. 59\u201370 (2005)","DOI":"10.1007\/11564126_11"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Davidson, I., Wagstaff, K.L., Basu, S.: Measuring constraint-set utility for partitional clustering algorithms. In: Proc. Europ. Conf. on Principles of Data Mining and Knowledge Discovery. pp. 115\u2013126 (2006)","DOI":"10.1007\/11871637_15"},{"key":"7_CR13","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1\u201330 (2006)","journal-title":"The Journal of Machine Learning Research"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Gan\u00e7arski, P., Dao, T.B.H., Cr\u00e9milleux, B., Forestier, G., Lampert, T.: Constrained clustering: Current and new trends. A Guided Tour of Artificial Intelligence Research: Volume II: AI Algorithms pp. 447\u2013484 (2020)","DOI":"10.1007\/978-3-030-06167-8_14"},{"issue":"1","key":"7_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006)","journal-title":"Mach. Learn."},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neuroimage.2012.09.065","volume":"65","author":"KR Gray","year":"2013","unstructured":"Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D.: Random forest-based similarity measures for multi-modal classification of alzheimer\u2019s disease. Neuroimage 65, 167\u2013175 (2013)","journal-title":"Neuroimage"},{"key":"7_CR17","first-page":"507","volume":"35","author":"L Grinsztajn","year":"2022","unstructured":"Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data? Adv. Neural. Inf. Process. Syst. 35, 507\u2013520 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR18","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/s10618-016-0480-z","volume":"31","author":"V Grossi","year":"2017","unstructured":"Grossi, V., Romei, A., Turini, F.: Survey on using constraints in data mining. Data Min. Knowl. Disc. 31, 424\u2013464 (2017)","journal-title":"Data Min. Knowl. Disc."},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Hong, Y., Kwong, S.: Learning assignment order of instances for the constrained k-means clustering algorithm. IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics) 39(2), 568\u2013574 (2008)","DOI":"10.1109\/TSMCB.2008.2006641"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification pp. 193\u2013218 (1985)","DOI":"10.1007\/BF01908075"},{"issue":"8","key":"7_CR21","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651\u2013666 (2010)","journal-title":"Pattern Recogn. Lett."},{"key":"7_CR22","unstructured":"Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc. (1988)"},{"issue":"3","key":"7_CR23","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM computing surveys (CSUR) 31(3), 264\u2013323 (1999)","journal-title":"ACM computing surveys (CSUR)"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Lelis, L., Sander, J.: Semi-supervised density-based clustering. In: Proc. Int. Conf. on Data Mining. pp. 842\u2013847 (2009)","DOI":"10.1109\/ICDM.2009.143"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Proc. Int. Conf. on Data Mining. pp. 413\u2013422 (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. on Knowledge Discovery from Data (TKDD) 6(1), 1\u201339 (2012)","DOI":"10.1145\/2133360.2133363"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Liu, H., Fu, Y.: Clustering with partition level side information. In: Proc. Int. Conf. on Data Mining. pp. 877\u2013882 (2015)","DOI":"10.1109\/ICDM.2015.18"},{"key":"7_CR28","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd, S.: Least squares quantization in pcm. IEEE Trans. on information theory 28, 129\u2013137 (1982)","journal-title":"IEEE Trans. on information theory"},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: Advances in neural information processing systems. pp. 985\u2013992 (2006)","DOI":"10.7551\/mitpress\/7503.003.0128"},{"key":"7_CR30","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3389\/frobt.2018.00018","volume":"5","author":"M Okabe","year":"2018","unstructured":"Okabe, M., Yamada, S.: Clustering using boosted constrained k-means algorithm. Frontiers in Robotics and AI 5, 18 (2018)","journal-title":"Frontiers in Robotics and AI"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Pei, Y., Fern, X.Z., Tjahja, T.V., Rosales, R.: Comparing clustering with pairwise and relative constraints: A unified framework. ACM Trans. on Knowledge Discovery from Data 11(2), 1\u201326 (2016)","DOI":"10.1145\/2996467"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Pelleg, D., Baras, D.: K-means with large and noisy constraint sets. In: Proc. European Conference on Machine Learning. pp. 674\u2013682 (2007)","DOI":"10.1007\/978-3-540-74958-5_67"},{"issue":"3","key":"7_CR33","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1007\/s10618-022-00820-9","volume":"36","author":"H Peng","year":"2022","unstructured":"Peng, H., Pavlidis, N.G.: Weighted sparse simplex representation: a unified framework for subspace clustering, constrained clustering, and active learning. Data Min. Knowl. Disc. 36(3), 958\u2013986 (2022)","journal-title":"Data Min. Knowl. Disc."},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Perbet, F., Stenger, B., Maki, A.: Random forest clustering and application to video segmentation. In: Proc. of British Machine Vision Conference. pp. 1\u201310 (2009)","DOI":"10.5244\/C.23.100"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Qian, P., Jiang, Y., Wang, S., Su, K.H., Wang, J., Hu, L., Muzic, R.F.: Affinity and penalty jointly constrained spectral clustering with all-compatibility, flexibility, and robustness. IEEE Trans. on neural networks and learning systems 28(5), 1123\u20131138 (2016)","DOI":"10.1109\/TNNLS.2015.2511179"},{"key":"7_CR36","unstructured":"Rangapuram, S.S., Hein, M.: Constrained 1-spectral clustering. In: Artificial Intelligence and Statistics. pp. 1143\u20131151. PMLR (2012)"},{"key":"7_CR37","doi-asserted-by":"crossref","unstructured":"Raniero, M., Bicego, M., Cicalese, F.: Distance-based random forest clustering with missing data. In: Proc. Int. Conf. on Image Analysis and Processing. pp. 121\u2013132. Springer (2022)","DOI":"10.1007\/978-3-031-06433-3_11"},{"issue":"3","key":"7_CR38","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1513\/AnnalsATS.201403-125OC","volume":"12","author":"SI Rennard","year":"2015","unstructured":"Rennard, S.I., Locantore, N., Delafont, B., Tal-Singer, R., Silverman, E.K., Vestbo, J., Miller, B.E., Bakke, P., Celli, B., Calverley, P.M., et al.: Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the eclipse cohort using cluster analysis. Ann. Am. Thorac. Soc. 12(3), 303\u2013312 (2015)","journal-title":"Ann. Am. Thorac. Soc."},{"issue":"1","key":"7_CR39","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1198\/106186006X94072","volume":"15","author":"T Shi","year":"2006","unstructured":"Shi, T., Horvath, S.: Unsupervised learning with random forest predictors. J. Comput. Graph. Stat. 15(1), 118\u2013138 (2006)","journal-title":"J. Comput. Graph. Stat."},{"key":"7_CR40","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/modpathol.3800322","volume":"18","author":"T Shi","year":"2005","unstructured":"Shi, T., Seligson, D., Belldegrun, A., Palotie, A., Horvath, S.: Tumor classification by tissue microarray profiling: Random forest clustering applied to renal cell carcinoma. Mod. Pathol. 18, 547\u2013557 (2005)","journal-title":"Mod. Pathol."},{"key":"7_CR41","doi-asserted-by":"crossref","unstructured":"Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition. pp.\u00a01\u20138 (2008)","DOI":"10.1109\/CVPR.2008.4587503"},{"key":"7_CR42","doi-asserted-by":"crossref","unstructured":"Ting, K., Zhu, Y., Carman, M., Zhu, Y., Zhou, Z.H.: Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure. In: Proc. Int. Conf. on Knowledge Discovery and Data mining. pp. 1205\u20131214 (2016)","DOI":"10.1145\/2939672.2939779"},{"key":"7_CR43","first-page":"1223","volume":"35","author":"M Tiwari","year":"2022","unstructured":"Tiwari, M., Kang, R., Lee, J., Piech, C., Shomorony, I., Thrun, S., Zhang, M.J.: Mabsplit: Faster forest training using multi-armed bandits. Adv. Neural. Inf. Process. Syst. 35, 1223\u20131237 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"4","key":"7_CR44","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1037\/0033-295X.84.4.327","volume":"84","author":"A Tversky","year":"1977","unstructured":"Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327 (1977)","journal-title":"Psychol. Rev."},{"issue":"2","key":"7_CR45","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373\u2013440 (2020)","journal-title":"Mach. Learn."},{"issue":"4","key":"7_CR46","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U von Luxburg","year":"2007","unstructured":"von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"key":"7_CR47","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.patrec.2020.11.015","volume":"142","author":"A Vouros","year":"2021","unstructured":"Vouros, A., Vasilaki, E.: A semi-supervised sparse k-means algorithm. Pattern Recogn. Lett. 142, 65\u201371 (2021)","journal-title":"Pattern Recogn. Lett."},{"key":"7_CR48","unstructured":"Wagstaff, K., Cardie, C., Rogers, S., Schr\u00f6dl, S.: Constrained k-means clustering with background knowledge. In: Proc. Int. Conf. on Machine Learning. vol.\u00a01, pp. 577\u2013584 (2001)"},{"key":"7_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10618-012-0291-9","volume":"28","author":"X Wang","year":"2014","unstructured":"Wang, X., Qian, B., Davidson, I.: On constrained spectral clustering and its applications. Data Min. Knowl. Disc. 28, 1\u201330 (2014)","journal-title":"Data Min. Knowl. Disc."},{"key":"7_CR50","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.csda.2013.04.010","volume":"66","author":"D Yan","year":"2013","unstructured":"Yan, D., Chen, A., Jordan, M.: Cluster forests. Computational Statistics & Data Analysis 66, 178\u2013192 (2013)","journal-title":"Computational Statistics & Data Analysis"},{"key":"7_CR51","doi-asserted-by":"crossref","unstructured":"Zhu, W., Nie, F., Li, X.: Fast spectral clustering with efficient large graph construction. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). pp. 2492\u20132496. IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7952605"},{"key":"7_CR52","doi-asserted-by":"crossref","unstructured":"Zhu, X., Loy, C., Gong, S.: Constructing robust affinity graphs for spectral clustering. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition. pp. 1450\u20131457 (2014)","DOI":"10.1109\/CVPR.2014.188"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78383-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T23:39:44Z","timestamp":1733096384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78383-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031783821","9783031783838"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78383-8_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}