{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:58:00Z","timestamp":1774627080050,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476063"],"award-info":[{"award-number":["62476063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102097"],"award-info":[{"award-number":["62102097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China-Research Grants Council of Hong Kong-Joint Research Scheme","award":["N_HKBU214\/21"],"award-info":[{"award-number":["N_HKBU214\/21"]}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515012855"],"award-info":[{"award-number":["2023A1515012855"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Research Fund of RGC","award":["12201321"],"award-info":[{"award-number":["12201321"]}]},{"name":"General Research Fund of RGC","award":["12202622"],"award-info":[{"award-number":["12202622"]}]},{"name":"General Research Fund of RGC","award":["12201323"],"award-info":[{"award-number":["12201323"]}]},{"name":"RGC Senior Research Fellow Scheme","award":["SRFS2324-2S02"],"award-info":[{"award-number":["SRFS2324-2S02"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01695-7","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T10:41:42Z","timestamp":1734604902000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Adaptive micro partition and hierarchical merging for accurate mixed data clustering"],"prefix":"10.1007","volume":"11","author":[{"given":"Yunfan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Rong","family":"Zou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0328-987X","authenticated-orcid":false,"given":"Yiqun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yiu-ming","family":"Cheung","sequence":"additional","affiliation":[]},{"given":"Kangshun","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"1695_CR1","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.jhydrol.2018.06.060","volume":"563","author":"X Hongshi","year":"2018","unstructured":"Hongshi X, Ma C, Lian J, Kui X, Chaima E (2018) Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou. Chin J Hydrol 563:975\u2013986. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.06.060","journal-title":"Chin J Hydrol"},{"key":"1695_CR2","doi-asserted-by":"publisher","unstructured":"Alan A (2002) Categorical data analysis. Wiley Series in Probability and Statistics. Wiley. https:\/\/doi.org\/10.1002\/0471249688","DOI":"10.1002\/0471249688"},{"key":"1695_CR3","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","volume":"622","author":"AM Ikotun","year":"2023","unstructured":"Ikotun AM, Ezugwu AE, Abualigah L, Abuhaija B, Heming J (2023) K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf Sci 622:178\u2013210. https:\/\/doi.org\/10.1016\/j.ins.2022.11.139","journal-title":"Inf Sci"},{"key":"1695_CR4","volume-title":"Studies in classification, data analysis, and knowledge organization","author":"P Arabie","year":"2006","unstructured":"Arabie P, Baier Newark D, Critchley Cottbus F, Keynes M (2006) Studies in classification, data analysis, and knowledge organization. Springer, New York"},{"key":"1695_CR5","unstructured":"Huang Z (1997) Clustering large data sets with mixed numeric and categorical values. In: Proceedings of the 1st Pacific-Asia conference on knowledge discovery and data mining, pp 21\u201334"},{"key":"1695_CR6","doi-asserted-by":"publisher","unstructured":"Mohamed ABHK, Chiheb-Eddine BN, Nadia E (2015) MapReduce-based k-prototypes clustering method for big data. In: Proceedings of the IEEE international conference on data science and advanced analytics, pages 1\u20137. IEEE. https:\/\/doi.org\/10.1109\/DSAA.2015.7344894","DOI":"10.1109\/DSAA.2015.7344894"},{"issue":"10","key":"1695_CR7","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1109\/TNNLS.2015.2451151","volume":"27","author":"Y Qian","year":"2016","unstructured":"Qian Y, Li F, Liang J, Liu B, Dang C (2016) Space structure and clustering of categorical data. IEEE Trans Neural Netw Learn Syst 27(10):2047\u20132059. https:\/\/doi.org\/10.1109\/TNNLS.2015.2451151","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"1695_CR8","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1109\/TKDE.2018.2848902","volume":"31","author":"S Jian","year":"2019","unstructured":"Jian S, Pang G, Cao L, Kai L, Gao H (2019) CURE: flexible categorical data representation by hierarchical coupling learning. IEEE Trans Knowl Data Eng 31(5):853\u2013866. https:\/\/doi.org\/10.1109\/TKDE.2018.2848902","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"1695_CR9","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1109\/TPAMI.2020.3010953","volume":"44","author":"C Zhu","year":"2022","unstructured":"Zhu C, Cao L, Yin J (2022) Unsupervised heterogeneous coupling learning for categorical representation. IEEE Trans Pattern Anal Mach Intell 44(1):533\u2013549. https:\/\/doi.org\/10.1109\/TPAMI.2020.3010953","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1695_CR10","unstructured":"Lin D (1998) An Information-Theoretic Definition of Similarity. In: Proceedings of the 15th International Conference on Machine Learning, pages 296\u2013304"},{"issue":"16","key":"1695_CR11","doi-asserted-by":"publisher","first-page":"2549","DOI":"10.1016\/j.patrec.2005.06.002","volume":"26","author":"SQ Le","year":"2005","unstructured":"Le SQ, Ho TB (2005) An association-based dissimilarity measure for categorical data. Pattern Recogn Lett 26(16):2549\u20132557. https:\/\/doi.org\/10.1016\/j.patrec.2005.06.002","journal-title":"Pattern Recogn Lett"},{"issue":"5","key":"1695_CR12","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","volume":"27","author":"JZ Huang","year":"2005","unstructured":"Huang JZ, Ng MK, Rong H, Li Z (2005) Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell 27(5):657\u2013668. https:\/\/doi.org\/10.1109\/TPAMI.2005.95","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1695_CR13","doi-asserted-by":"publisher","unstructured":"Ienco D, Pensa Ruggero G, Meo R (2009) Context-Based Distance Learning for Categorical Data Clustering. In: Advances in Intelligent Data Analysis VIII 83\u201394. https:\/\/doi.org\/10.1007\/978-3-642-03915-7_8","DOI":"10.1007\/978-3-642-03915-7_8"},{"issue":"1","key":"1695_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133361","volume":"6","author":"D Ienco","year":"2012","unstructured":"Ienco D, Pensa RG, Meo R (2012) From context to distance. ACM Trans Knowl Discov Data 6(1):1\u201325. https:\/\/doi.org\/10.1145\/2133360.2133361","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"5","key":"1695_CR15","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1109\/TNNLS.2015.2436432","volume":"27","author":"H Jia","year":"2016","unstructured":"Jia H, Cheung Y, Liu J (2016) A new distance metric for unsupervised learning of categorical data. IEEE Trans Neural Netw Learn Syst 27(5):1065\u20131079. https:\/\/doi.org\/10.1109\/TNNLS.2015.2436432","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"1695_CR16","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/TNNLS.2019.2899381","volume":"31","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Cheung Y-M, Tan KC (2020) A unified entropy-based distance metric for ordinal-and-nominal-attribute data clustering. IEEE Trans Neural Netw Learn Syst 31(1):39\u201352. https:\/\/doi.org\/10.1109\/TNNLS.2019.2899381","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1695_CR17","doi-asserted-by":"publisher","unstructured":"Zhang Y, Cheung Y (2020) An ordinal data clustering algorithm with automated distance learning. In: Proceedings of the AAAI Conference on Artificial Intelligence 34:6869\u20136876. https:\/\/doi.org\/10.1609\/AAAI.V34I04.6168","DOI":"10.1609\/AAAI.V34I04.6168"},{"issue":"2","key":"1695_CR18","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1109\/TCYB.2020.2983073","volume":"52","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Cheung Y-M (2022) A new distance metric exploiting heterogeneous interattribute relationship for ordinal-and-nominal-attribute data clustering. IEEE Trans Cybern 52(2):758\u2013771. https:\/\/doi.org\/10.1109\/TCYB.2020.2983073","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"1695_CR19","doi-asserted-by":"publisher","first-page":"3560","DOI":"10.1109\/TPAMI.2021.3056510","volume":"44","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Cheung Y (2022) Learnable weighting of intra-attribute distances for categorical data clustering with nominal and ordinal attributes. IEEE Trans Pattern Anal Mach Intell 44(7):3560\u20133576. https:\/\/doi.org\/10.1109\/TPAMI.2021.3056510","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"1695_CR20","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.1016\/j.patcog.2013.01.027","volume":"46","author":"Y Cheung","year":"2013","unstructured":"Cheung Y, Jia H (2013) Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number. Pattern Recogn 46(8):2228\u20132238. https:\/\/doi.org\/10.1016\/j.patcog.2013.01.027","journal-title":"Pattern Recogn"},{"key":"1695_CR21","doi-asserted-by":"publisher","unstructured":"Zhu C, Zhang Q, Cao L, Abrahamyan A (2020) Mix2Vec: unsupervised mixed data representation. In: Proceedings of the IEEE 7th International Conference on Data Science and Advanced Analytics, pp 118\u2013127. IEEE. https:\/\/doi.org\/10.1109\/DSAA49011.2020.00024","DOI":"10.1109\/DSAA49011.2020.00024"},{"key":"1695_CR22","doi-asserted-by":"publisher","unstructured":"Zhang Y, Cheung Y, Zeng A (2022) Het2Hom: representation of heterogeneous attributes into homogeneous concept spaces for categorical-and-numerical-attribute data clustering. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pages 3758\u20133765, California. https:\/\/doi.org\/10.24963\/ijcai.2022\/522","DOI":"10.24963\/ijcai.2022\/522"},{"issue":"3","key":"1695_CR23","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264\u2013323. https:\/\/doi.org\/10.1145\/331499.331504","journal-title":"ACM Comput Surv"},{"key":"1695_CR24","doi-asserted-by":"publisher","unstructured":"Alamuri M, Surampudi BR, Negi A (2014) A survey of distance\/similarity measures for categorical data. In: Proceedings of the International Joint Conference on Neural Networks, pages 1907\u20131914. IEEE. https:\/\/doi.org\/10.1109\/IJCNN.2014.6889941","DOI":"10.1109\/IJCNN.2014.6889941"},{"issue":"1","key":"1695_CR25","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.patrec.2006.06.006","volume":"28","author":"A Ahmad","year":"2007","unstructured":"Ahmad A, Dey L (2007) A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set. Pattern Recogn Lett 28(1):110\u2013118. https:\/\/doi.org\/10.1016\/j.patrec.2006.06.006","journal-title":"Pattern Recogn Lett"},{"issue":"9","key":"1695_CR26","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1109\/TKDE.2018.2808532","volume":"30","author":"S Jian","year":"2018","unstructured":"Jian S, Cao L, Kai L, Gao H (2018) Unsupervised Coupled Metric Similarity for Non-IID Categorical Data. IEEE Trans Knowl Data Eng 30(9):1810\u20131823. https:\/\/doi.org\/10.1109\/TKDE.2018.2808532","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"1695_CR27","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s40747-020-00191-y","volume":"7","author":"L Chen","year":"2021","unstructured":"Chen L, Guo Q, Liu Z, Zhang S, Zhang H (2021) Enhanced synchronization-inspired clustering for high-dimensional data. Complex Intell Syst 7(1):203\u2013223. https:\/\/doi.org\/10.1007\/s40747-020-00191-y","journal-title":"Complex Intell Syst"},{"key":"1695_CR28","doi-asserted-by":"publisher","unstructured":"Zhang Y, Cheung Y (2018) Exploiting order information embedded in ordered categories for ordinal data clustering. In: Proceedings of the 24th International Symposium on Methodologies for Intelligent Systems, pp. 247\u2013257. https:\/\/doi.org\/10.1007\/978-3-030-01851-1_24","DOI":"10.1007\/978-3-030-01851-1_24"},{"key":"1695_CR29","doi-asserted-by":"crossref","unstructured":"Wang P, Zhang Y, Zhang Y, Lu Y, Li M, Cheung Y (2024) Clustering by learning the ordinal relationships of qualitative attribute values. In: Proceedings of the IEEE 34th International Joint Conference on Neural Networks, pp 1\u20138","DOI":"10.1109\/IJCNN60899.2024.10650134"},{"key":"1695_CR30","doi-asserted-by":"crossref","unstructured":"Zhang Y, Luo X, Chen Q, Zou R, Zhang Y, Cheung Y (2024) Towards unbiased minimal cluster analysis of categorical-and-numerical attribute data. In: Proceedings of the 28th International Conference on Pattern Recognition, pages 1\u201316","DOI":"10.1007\/978-3-031-78166-7_17"},{"key":"1695_CR31","doi-asserted-by":"publisher","unstructured":"Sangma JW, Sarkar M, Pal V, Agrawal A, Yogita (2022) Hierarchical clustering for multiple nominal data streams with evolving behaviour. Complex Intell Syst 8(2):1737\u20131761. https:\/\/doi.org\/10.1007\/s40747-021-00634-0","DOI":"10.1007\/s40747-021-00634-0"},{"key":"1695_CR32","doi-asserted-by":"crossref","unstructured":"Zhao M, Feng S, Zhang Y, Mengkei L, Lu Y, Cheung Y (2024) Learning order forest for qualitative-attribute data clustering. In: Proceedings of the 27th European Conference on Artificial Intelligence, pages 1\u20138","DOI":"10.3233\/FAIA240709"},{"issue":"6","key":"1695_CR33","doi-asserted-by":"publisher","first-page":"7451","DOI":"10.1007\/s40747-023-01135-y","volume":"9","author":"Z Peng","year":"2023","unstructured":"Peng Z, Song X, Song S, Stojanovic V (2023) Hysteresis quantified control for switched reaction\u2013diffusion systems and its application. Complex Intell Syst 9(6):7451\u20137460. https:\/\/doi.org\/10.1007\/s40747-023-01135-y","journal-title":"Complex Intell Syst"},{"issue":"3","key":"1695_CR34","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1002\/acs.3546","volume":"37","author":"P Sun","year":"2023","unstructured":"Sun P, Song X, Song S, Stojanovic V (2023) Composite adaptive finite-time fuzzy control for switched nonlinear systems with preassigned performance. Int J Adapt Control Signal Process 37(3):771\u2013789. https:\/\/doi.org\/10.1002\/acs.3546","journal-title":"Int J Adapt Control Signal Process"},{"issue":"2","key":"1695_CR35","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1002\/acs.3529","volume":"37","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Song X, Sun X, Stojanovic V (2023) Hybrid-driven-based fuzzy secure filtering for nonlinear parabolic partial differential equation systems with cyber attacks. Int J Adapt Control Signal Process 37(2):380\u2013398. https:\/\/doi.org\/10.1002\/acs.3529","journal-title":"Int J Adapt Control Signal Process"},{"key":"1695_CR36","unstructured":"Abdel-Basset M, Abouhawwash M, Askar S, Hawash H, Nayyar A (2023) A system of the internet of nano-dental things"},{"key":"1695_CR37","doi-asserted-by":"crossref","unstructured":"Shenghong C, Yiqun Z, Xiaopeng L, Yiu-ming C, Hong J, Peng L (2024) Robust categorical data clustering guided by multi-granular competitive learning. In: Proceedings of the IEEE 44th International Conference on Distributed Computing Systems, pp. 288\u2013299","DOI":"10.1109\/ICDCS60910.2024.00035"},{"key":"1695_CR38","doi-asserted-by":"crossref","unstructured":"Zou R, Zhang Y, Zhang Y, Lu Y, Li M, Cheung Y (2024) Federated clustering with unknown number of clusters. In: Proceedings of the IEEE 6th International Conference on Data-driven Optimization of Complex Systems, pp. 671\u2013677","DOI":"10.1109\/DOCS63458.2024.10704350"},{"key":"1695_CR39","doi-asserted-by":"crossref","unstructured":"Chen J, Ji Y, Zou R, Zhang Y, Cheung Y (2024) QGRL: quaternion graph representation learning for heterogeneous feature data clustering. In: Proceedings of the 30th SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1\u201310","DOI":"10.1145\/3637528.3671839"},{"key":"1695_CR40","doi-asserted-by":"publisher","unstructured":"Mohamed M (2023) Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories. Sustain Mach Intell J 4. https:\/\/doi.org\/10.61185\/SMIJ.2023.44102","DOI":"10.61185\/SMIJ.2023.44102"},{"key":"1695_CR41","doi-asserted-by":"publisher","unstructured":"Mohamed M (2023) Empowering deep learning based organizational decision making: a survey. Sustain Mach Intell J 3. https:\/\/doi.org\/10.61185\/SMIJ.2023.33105","DOI":"10.61185\/SMIJ.2023.33105"},{"issue":"1\/2","key":"1695_CR42","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.aci.2019.01.001","volume":"18","author":"N AlNuaimi","year":"2022","unstructured":"AlNuaimi N, Masud MM, Serhani MA, Zaki N (2022) Streaming feature selection algorithms for big data: a survey. Appl Comput Inf 18(1\/2):113\u2013135. https:\/\/doi.org\/10.1016\/j.aci.2019.01.001","journal-title":"Appl Comput Inf"},{"key":"1695_CR43","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.ijar.2015.11.006","volume":"69","author":"S Eskandari","year":"2016","unstructured":"Eskandari S, Javidi MM (2016) Online streaming feature selection using rough sets. Int J Approx Reason 69:35\u201357. https:\/\/doi.org\/10.1016\/j.ijar.2015.11.006","journal-title":"Int J Approx Reason"},{"issue":"3","key":"1695_CR44","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s10044-018-0690-7","volume":"22","author":"Mohammad Masoud Javidi and Sadegh Eskandari","year":"2019","unstructured":"Mohammad Masoud Javidi and Sadegh Eskandari (2019) Online streaming feature selection: a minimum redundancy, maximum significance approach. Pattern Anal Appl 22(3):949\u2013963. https:\/\/doi.org\/10.1007\/s10044-018-0690-7","journal-title":"Pattern Anal Appl"},{"issue":"4","key":"1695_CR45","doi-asserted-by":"publisher","first-page":"2821","DOI":"10.1007\/s10462-021-10072-6","volume":"55","author":"J Sa\u00fal Solorio-Fern\u00e1ndez","year":"2022","unstructured":"Sa\u00fal Solorio-Fern\u00e1ndez J, Carrasco-Ochoa A, Mart\u00ednez-Trinidad JF (2022) A survey on feature selection methods for mixed data. Artif Intell Rev 55(4):2821\u20132846. https:\/\/doi.org\/10.1007\/s10462-021-10072-6","journal-title":"Artif Intell Rev"},{"key":"1695_CR46","doi-asserted-by":"publisher","unstructured":"Zhang P, Li T, Yuan Z, Luo C, Liu K, Yang X (2022) Heterogeneous Feature Selection Based on Neighborhood Combination Entropy. IEEE Trans Neural Netw Learn Syst, pp. 1\u201314. https:\/\/doi.org\/10.1109\/TNNLS.2022.3193929","DOI":"10.1109\/TNNLS.2022.3193929"},{"issue":"2","key":"1695_CR47","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1023\/A:1026543900054","volume":"40","author":"Y Rubner","year":"2000","unstructured":"Rubner Y, Tomasi C, Guibas LJ (2000) The Earth Mover\u2019s Distance as a Metric for Image Retrieval. Int J Comput Vision 40(2):99. https:\/\/doi.org\/10.1023\/A:1026543900054","journal-title":"Int J Comput Vision"},{"issue":"8","key":"1695_CR48","doi-asserted-by":"publisher","first-page":"2148","DOI":"10.1109\/TKDE.2015.2411281","volume":"27","author":"X Jia","year":"2015","unstructured":"Jia X, Bin Lei YG, Winslett M, Ge Yu, Zhang Z (2015) Efficient Similarity Join Based on Earth Mover\u2019s Distance Using MapReduce. IEEE Trans Knowl Data Eng 27(8):2148\u20132162. https:\/\/doi.org\/10.1109\/TKDE.2015.2411281","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"1695_CR49","doi-asserted-by":"publisher","first-page":"6530","DOI":"10.1109\/TNNLS.2022.3202700","volume":"34","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Cheung Y-M (2023) Graph-based dissimilarity measurement for cluster analysis of any-type-attributed data. IEEE Trans Neural Netw Learn Syst 34(9):6530\u20136544. https:\/\/doi.org\/10.1109\/TNNLS.2022.3202700","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"1695_CR50","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1023\/A:1009769707641","volume":"2","author":"Z Huang","year":"1998","unstructured":"Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Disc 2(3):283\u2013304. https:\/\/doi.org\/10.1023\/A:1009769707641","journal-title":"Data Min Knowl Disc"},{"key":"1695_CR51","unstructured":"He Xiaofei, Cai Deng, Niyogi Partha (2005) Laplacian Score for Feature Selection. In Advances in Neural Information Processing Systems, pages 507\u2013514"},{"key":"1695_CR52","doi-asserted-by":"publisher","unstructured":"Santos Jorge M, Mark Embrechts (2009) On the use of the adjusted rand index as a metric for evaluating supervised classification. In: Proceedings of the 19th International conference on artificial neural networks, pages 175\u2013184. https:\/\/doi.org\/10.1007\/978-3-642-04277-5_18","DOI":"10.1007\/978-3-642-04277-5_18"},{"issue":"336","key":"1695_CR53","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand WM (1971) Objective Criteria for the Evaluation of Clustering Methods. J Am Stat Assoc 66(336):846\u2013850","journal-title":"J Am Stat Assoc"},{"key":"1695_CR54","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.5555\/3122009.3176831","volume":"18","author":"AJ Gates","year":"2017","unstructured":"Gates AJ, Ahn YY (2017) The impact of random models on clustering similarity. J Mach Learn Res 18:3049\u20133076. https:\/\/doi.org\/10.5555\/3122009.3176831","journal-title":"J Mach Learn Res"},{"key":"1695_CR55","unstructured":"van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(86):2579\u20132605"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01695-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01695-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01695-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:17:11Z","timestamp":1738268231000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01695-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1695"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01695-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"5 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"84"}}