{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:04:33Z","timestamp":1777658673192,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Scientific Research Funds of Huaqiao University","award":["21BS122"],"award-info":[{"award-number":["21BS122"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s13042-024-02104-8","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T08:02:52Z","timestamp":1709539372000},"page":"3471-3494","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A fast DBSCAN algorithm using a bi-directional HNSW index structure for big data"],"prefix":"10.1007","volume":"15","author":[{"given":"Shaoyuan","family":"Weng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongwen","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-5637","authenticated-orcid":false,"given":"Jin","family":"Gou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"2104_CR1","doi-asserted-by":"publisher","first-page":"4758","DOI":"10.1007\/s10489-021-02561-6","volume":"52","author":"L Cai","year":"2021","unstructured":"Cai L, Zhu L, Jiang F, Zhang Y, He J (2021) Research on multi-source poi data fusion based on ontology and clustering algorithms. Appl Intell 52:4758\u20134774","journal-title":"Appl Intell"},{"key":"2104_CR2","doi-asserted-by":"crossref","unstructured":"Liu Y, Wenxuan T, Zhou S, Liu X, Song L, Yang X, Zhu E (2022) Deep graph clustering via dual correlation reduction. In: Proceedings of the AAAI conference on artificial intelligence, vol 36. pp 7603\u20137611","DOI":"10.1609\/aaai.v36i7.20726"},{"key":"2104_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2022.111943","volume":"261","author":"K Li","year":"2022","unstructured":"Li K, Zhang J, Chen X, Xue W (2022) Building\u2019s hourly electrical load prediction based on data clustering and ensemble learning strategy. Energy Build 261:111943","journal-title":"Energy Build"},{"issue":"9","key":"2104_CR4","doi-asserted-by":"publisher","first-page":"1851","DOI":"10.1007\/s11517-021-02418-7","volume":"59","author":"ME Brasch","year":"2021","unstructured":"Brasch ME, Pe\u00f1a AN, Henderson JH (2021) Image-based cell subpopulation identification through automated cell tracking, principal component analysis, and partitioning around medoids clustering. Med Biol Eng Comput 59(9):1851\u20131864","journal-title":"Med Biol Eng Comput"},{"key":"2104_CR5","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1007\/s11831-021-09629-z","volume":"29","author":"K Gopal Dhal","year":"2021","unstructured":"Gopal Dhal K, Das A, Ray S, Sarkar K, G\u00e1lvez J (2021) An analytical review on rough set based image clustering. Arch Comput Methods Eng 29:1643\u20131672","journal-title":"Arch Comput Methods Eng"},{"key":"2104_CR6","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.inffus.2020.08.003","volume":"65","author":"A Belhadi","year":"2021","unstructured":"Belhadi A, Djenouri Y, Srivastava G, Djenouri D, Lin JC-W, Fortino G (2021) Deep learning for pedestrian collective behavior analysis in smart cities: a model of group trajectory outlier detection. Inf Fusion 65:13\u201320","journal-title":"Inf Fusion"},{"issue":"1","key":"2104_CR7","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/jtaer17010003","volume":"17","author":"A-L Scutariu","year":"2022","unstructured":"Scutariu A-L, \u015eu\u015fu \u015e, Huidumac-Petrescu C-E, Gogonea R-M (2022) A cluster analysis concerning the behavior of enterprises with e-commerce activity in the context of the COVID-19 pandemic. J Theor Appl Electron Commer Res 17(1):47\u201368","journal-title":"J Theor Appl Electron Commer Res"},{"key":"2104_CR8","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s40031-021-00651-0","volume":"103","author":"TH Sardar","year":"2021","unstructured":"Sardar TH, Ansari Z (2021) MapReduce-based fuzzy C-means algorithm for distributed document clustering. J Inst Eng (India) Ser B 103:131\u2013142","journal-title":"J Inst Eng (India) Ser B"},{"key":"2104_CR9","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patrec.2022.04.004","volume":"158","author":"M Bibi","year":"2022","unstructured":"Bibi M, Abbasi WA, Aziz W, Khalil S, Uddin M, Iwendi C, Gadekallu TR (2022) A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis. Pattern Recognit Lett 158:80\u201386","journal-title":"Pattern Recognit Lett"},{"key":"2104_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114264","volume":"168","author":"S Singh","year":"2021","unstructured":"Singh S, Ganie AH (2021) Applications of picture fuzzy similarity measures in pattern recognition, clustering, and MADM. Expert Syst Appl 168:114264","journal-title":"Expert Syst Appl"},{"key":"2104_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104743","volume":"110","author":"AE Ezugwu","year":"2022","unstructured":"Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA (2022) A comprehensive survey of clustering algorithms: state-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell 110:104743","journal-title":"Eng Appl Artif Intell"},{"issue":"34","key":"2104_CR12","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester M, Kriegel H-P, Sander J, Xiaowei X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press 96(34):226\u2013231","journal-title":"AAAI Press"},{"key":"2104_CR13","unstructured":"Kassambara A (2017) Practical guide to cluster analysis in R: unsupervised machine learning, vol 1. Sthda"},{"key":"2104_CR14","doi-asserted-by":"publisher","first-page":"47468","DOI":"10.1109\/ACCESS.2020.2972034","volume":"8","author":"S-S Li","year":"2020","unstructured":"Li S-S (2020) An improved DBSCAN algorithm based on the neighbor similarity and fast nearest neighbor query. IEEE Access 8:47468\u201347476","journal-title":"IEEE Access"},{"key":"2104_CR15","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.procs.2019.01.208","volume":"147","author":"W Jing","year":"2019","unstructured":"Jing W, Zhao C, Jiang C (2019) An improvement method of DBSCAN algorithm on cloud computing. Procedia Comput Sci 147:596\u2013604","journal-title":"Procedia Comput Sci"},{"key":"2104_CR16","doi-asserted-by":"crossref","unstructured":"Chen Y , Zhou L, Bouguila N, Zhong B, Wu F, Lei Z, Du J, Li H (2018) Semi-convex hull tree: fast nearest neighbor queries for large scale data on GPUS. In: 2018 IEEE international conference on data mining (ICDM), IEEE. pp 911\u2013916","DOI":"10.1109\/ICDM.2018.00110"},{"issue":"2","key":"2104_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3503513","volume":"8","author":"M Xiao","year":"2022","unstructured":"Xiao M, Wang H, Geng L, Lee R, Zhang X (2022) An RDMA-enabled in-memory computing platform for R-tree on clusters. ACM Trans Spatial Algorithms and Syst (TSAS) 8(2):1\u201326","journal-title":"ACM Trans Spatial Algorithms and Syst (TSAS)"},{"key":"2104_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109238","volume":"136","author":"S Ding","year":"2023","unstructured":"Ding S, Li C, Xiao X, Ding L, Zhang J, Guo L, Shi T (2023) A sampling-based density peaks clustering algorithm for large-scale data. Pattern Recognit 136:109238","journal-title":"Pattern Recognit"},{"key":"2104_CR19","unstructured":"Weng S , Gou J , Fan Z (2021) $$h$$-DBSCAN: a simple fast DBSCAN algorithm for big data. In: Asian conference on machine learning. PMLR. pp 81\u201396"},{"key":"2104_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108768","volume":"129","author":"W Cao","year":"2022","unstructured":"Cao W, Zhang Z, Liu C, Li R, Jiao Q, Zhiwen Yu, Wong H-S (2022) Unsupervised discriminative feature learning via finding a clustering-friendly embedding space. Pattern Recognit 129:108768","journal-title":"Pattern Recognit"},{"key":"2104_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110530","volume":"270","author":"S Yang","year":"2023","unstructured":"Yang S, Verma S, Cai B, Jiang J, Yu K, Chen F, Yu S (2023) Variational co-embedding learning for attributed network clustering. Knowl Based Syst 270:110530","journal-title":"Knowl Based Syst"},{"key":"2104_CR22","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.patcog.2016.03.008","volume":"58","author":"K Mahesh Kumar","year":"2016","unstructured":"Mahesh Kumar K, Rama Mohan Reddy A (2016) A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method. Pattern Recognit 58:39\u201348","journal-title":"Pattern Recognit"},{"key":"2104_CR23","doi-asserted-by":"crossref","unstructured":"Vadapalli S, Valluri SR, Karlapalem K (2006) A simple yet effective data clustering algorithm. In: Sixth international conference on data mining (ICDM\u201906). IEEE. pp 1108\u20131112","DOI":"10.1109\/ICDM.2006.9"},{"key":"2104_CR24","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2015.05.109","volume":"171","author":"Y Lv","year":"2016","unstructured":"Lv Y, Ma T, Tang M, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2016) An efficient and scalable density-based clustering algorithm for datasets with complex structures. Neurocomputing 171:9\u201322","journal-title":"Neurocomputing"},{"issue":"6","key":"2104_CR25","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1109\/TKDE.2017.2787640","volume":"30","author":"A Bryant","year":"2017","unstructured":"Bryant A, Cios K (2017) RNN-DBSCAN: a density-based clustering algorithm using reverse nearest neighbor density estimates. IEEE Trans Knowl Data Eng 30(6):1109\u20131121","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"2104_CR26","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1109\/TPAMI.2018.2889473","volume":"42","author":"YA Malkov","year":"2018","unstructured":"Malkov YA, Yashunin DA (2018) Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans Pattern Anal Mach Intell 42(4):824\u2013836","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2104_CR27","doi-asserted-by":"publisher","first-page":"3695323","DOI":"10.1155\/2017\/3695323","volume":"2017","author":"Q He","year":"2017","unstructured":"He Q, Gu HX, Wei Q, Wang X (2017) A novel DBSCAN based on binary local sensitive hashing and binary-KNN representation. Adv Multimedia 2017:3695323","journal-title":"Adv Multimedia"},{"key":"2104_CR28","unstructured":"Tsai C-F, Wu C-T, Chen S (2009) GF-DBSCAN: a new efficient and effective data clustering technique for large databases. In: World scientific and engineering academy and society (WSEAS). pp 231\u2013236"},{"key":"2104_CR29","doi-asserted-by":"crossref","unstructured":"Mai ST, Assent I, Storgaard M (2016) AnyDBC: an efficient anytime density-based clustering algorithm for very large complex datasets. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp 1025\u20131034","DOI":"10.1145\/2939672.2939750"},{"key":"2104_CR30","doi-asserted-by":"crossref","unstructured":"Sarma A, Goyal P, Kumari S, Wani A, Challa JS, Islam S, Goyal N (2019) $$\\mu$$DBSCAN: an exact scalable DBSCAN algorithm for big data exploiting spatial locality. In: 2019 IEEE international conference on cluster computing (CLUSTER)","DOI":"10.1109\/CLUSTER.2019.8891020"},{"issue":"1","key":"2104_CR31","doi-asserted-by":"publisher","first-page":"4-es","DOI":"10.1145\/1217299.1217303","volume":"1","author":"A Gionis","year":"2007","unstructured":"Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Discov Data (TKDD) 1(1):4-es","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"issue":"8","key":"2104_CR32","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/2.781637","volume":"32","author":"G Karypis","year":"1999","unstructured":"Karypis G, Han E-H, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. Computer 32(8):68\u201375","journal-title":"Computer"},{"issue":"1","key":"2104_CR33","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.patcog.2007.04.010","volume":"41","author":"H Chang","year":"2008","unstructured":"Chang H, Yeung D-Y (2008) Robust path-based spectral clustering. Pattern Recognit 41(1):191\u2013203","journal-title":"Pattern Recognit"},{"issue":"1","key":"2104_CR34","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/T-C.1971.223083","volume":"100","author":"CT Zahn","year":"1971","unstructured":"Zahn CT (1971) Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comput 100(1):68\u201386","journal-title":"IEEE Trans Comput"},{"issue":"9","key":"2104_CR35","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TPAMI.2002.1033218","volume":"24","author":"CJ Veenman","year":"2002","unstructured":"Veenman CJ, Reinders MJT, Backer E (2002) A maximum variance cluster algorithm. IEEE Trans Pattern Anal Mach Intell 24(9):1273\u20131280","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2104_CR36","doi-asserted-by":"crossref","unstructured":"Kaul M, Yang B, Jensen CS (2013) Building accurate 3D spatial networks to enable next generation intelligent transportation systems. In 2013 IEEE 14th international conference on mobile data management, vol 1. IEEE. pp 137\u2013146","DOI":"10.1109\/MDM.2013.24"},{"issue":"4","key":"2104_CR37","doi-asserted-by":"publisher","first-page":"04017015","DOI":"10.1061\/(ASCE)IS.1943-555X.0000372","volume":"23","author":"M Pregnolato","year":"2017","unstructured":"Pregnolato M, Ford A, Glenis V, Wilkinson S, Dawson R (2017) Impact of climate change on disruption to urban transport networks from pluvial flooding. J Infrastruct Syst 23(4):04017015","journal-title":"J Infrastruct Syst"},{"key":"2104_CR38","doi-asserted-by":"publisher","first-page":"4308","DOI":"10.1038\/ncomms5308","volume":"5","author":"P Baldi","year":"2014","unstructured":"Baldi P, Sadowski P, Whiteson D (2014) Searching for exotic particles in high-energy physics with deep learning. Nat Commun 5:4308","journal-title":"Nat Commun"},{"key":"2104_CR39","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Iris"},{"key":"2104_CR40","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Cardiotocography"},{"key":"2104_CR41","doi-asserted-by":"crossref","unstructured":"Alimoglu F, Alpaydin E (1997) Combining multiple representations and classifiers for pen-based handwritten digit recognition. In: ICDAR. pp 637\u2013640","DOI":"10.1109\/ICDAR.1997.620583"},{"key":"2104_CR42","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Ecoli"},{"key":"2104_CR43","unstructured":"Martiniano A, Ferreira RP, Sassi RJ, Affonso C (2012) Application of a neuro fuzzy network in prediction of absenteeism at work. In: 2012 7th Iberian conference on information systems and technologies (CISTI). pp 1\u20134"},{"key":"2104_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104344","volume":"25","author":"FM Palechor","year":"2019","unstructured":"Palechor FM, Manotas A (2019) Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico. Data Brief 25:104344","journal-title":"Data Brief"},{"key":"2104_CR45","unstructured":"Sah P, Fokou\u00c3 E (2019) What do Asian religions have in common? An unsupervised text analytics exploration"},{"key":"2104_CR46","doi-asserted-by":"crossref","unstructured":"Diaz J, Colonna JG, Soares RB, Figueiredo C, Nakamura EF (2012) Compressive sensing for efficiently collecting wildlife sounds with wireless sensor networks. In: 21st International conference on computer communications and networks (ICCCN). pp 1\u20137","DOI":"10.1109\/ICCCN.2012.6289298"},{"key":"2104_CR47","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1093\/mnras\/stw656","volume":"459","author":"RJ Lyon","year":"2016","unstructured":"Lyon RJ, Stappers BW, Cooper S, Brooke JM, Knowles JD (2016) Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach. Mon Not R Astron Soc 459:1104\u20131123","journal-title":"Mon Not R Astron Soc"},{"issue":"1","key":"2104_CR48","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert L, Arabie P (1985) Comparing partitions. J Classif 2(1):193\u2013218","journal-title":"J Classif"},{"key":"2104_CR49","volume-title":"Elements of information theory. Wiley series in expert system applications to telecommunications","author":"TM Cover","year":"1991","unstructured":"Cover TM, Thomas JA, Bellamy J, Freeman RL, Liebowitz J (1991) Elements of information theory. Wiley series in expert system applications to telecommunications. Wiley, New York"},{"key":"2104_CR50","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.1007\/s10489-021-02421-3","volume":"52","author":"Z Fan","year":"2022","unstructured":"Fan Z, Chiong R, Chiong F (2022) A fuzzy-weighted gaussian kernel-based machine learning approach for body fat prediction. Appl Intel 52:2359\u20132368","journal-title":"Appl Intel"},{"key":"2104_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119558","volume":"217","author":"Z Fan","year":"2023","unstructured":"Fan Z, Gou J (2023) Predicting body fat using a novel fuzzy-weighted approach optimized by the whale optimization algorithm. Expert Syst Appl 217:119558","journal-title":"Expert Syst Appl"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02104-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02104-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02104-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T07:34:58Z","timestamp":1719992098000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02104-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":51,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["2104"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02104-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"29 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publications"}}]}}