{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T05:40:05Z","timestamp":1777009205335,"version":"3.51.4"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"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":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-026-02767-5","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:49:38Z","timestamp":1777006178000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Orthogonal factor-based biclustering algorithm (BCBOF) for high-dimensional data and its application in stock trend prediction"],"prefix":"10.1007","volume":"68","author":[{"given":"Yan","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da-Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"2767_CR1","unstructured":"Cheng Y, Church GM (2000) Biclustering of expression data. In: Proceedings of the Eighth international conference on intelligent systems for molecular biology, Vol 8, pp. 93\u2013103, AAAI Press"},{"key":"2767_CR2","unstructured":"Yang J, Wang HX, Wang W, Yu P (2003) Enhanced biclustering on expression data. In: Third IEEE Symposium on bioinformatics and bioengineering, 2003. Proceedings., pp. 321\u2013327, Bethesda, MD, USA"},{"key":"2767_CR3","doi-asserted-by":"crossref","unstructured":"Bergmann S, Ihmels J, Barkai N (2002) The iterative signature algorithm for the analysis of large scale gene expression data","DOI":"10.1103\/PhysRevE.67.031902"},{"issue":"15","key":"2767_CR4","doi-asserted-by":"publisher","first-page":"e101","DOI":"10.1093\/nar\/gkp491","volume":"37","author":"G Li","year":"2009","unstructured":"Li G, Ma Q, Tang H, Paterson AH, Ying X (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Res 37(15):e101\u2013e101","journal-title":"Nucleic Acids Res"},{"key":"2767_CR5","doi-asserted-by":"crossref","unstructured":"Brijesh K, Sriwastava (2023) Rubic: rapid unsupervised biclustering. BMC Bioinform 24:435","DOI":"10.1186\/s12859-023-05534-3"},{"issue":"1","key":"2767_CR6","first-page":"61","volume":"12","author":"L Lazzeroni","year":"2002","unstructured":"Lazzeroni L, Owen A (2002) Plaid models for gene expression data. Stat Sin 12(1):61\u201386","journal-title":"Stat Sin"},{"issue":"Suppl 1","key":"2767_CR7","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1471-2164-9-S1-S4","volume":"9","author":"J Gu","year":"2008","unstructured":"Gu J, Liu JS (2008) Bayesian biclustering of gene expression data. BMC Genomics 9(Suppl 1):S4","journal-title":"BMC Genomics"},{"issue":"9","key":"2767_CR8","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1093\/bioinformatics\/btl060","volume":"22","author":"A Prelic","year":"2006","unstructured":"Prelic A, Bleuler S, Zimmermann P, Wille A, Gruissem W, Hennig L, Thiele L, Zitzler E (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9):1122\u20131129","journal-title":"Bioinformatics"},{"issue":"4","key":"2767_CR9","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1101\/gr.648603","volume":"13","author":"Y Kluger","year":"2003","unstructured":"Kluger Y, Basri R, Chang JT, Gerstein M (2003) Spectral biclustering of microarray data: Coclustering genes and conditions. Genome Res 13(4):703\u2013716","journal-title":"Genome Res"},{"issue":"3","key":"2767_CR10","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1002\/sam.10123","volume":"4","author":"AJ Izenman","year":"2011","unstructured":"Izenman AJ, Harris PW, Mennis J, Jupin J, Obradovic Z (2011) Local spatial biclustering and prediction of urban juvenile delinquency and recidivism. Stat Anal Data Min ASA Data Sci J 4(3):259\u2013275","journal-title":"Stat Anal Data Min ASA Data Sci J"},{"key":"2767_CR11","doi-asserted-by":"publisher","first-page":"113991","DOI":"10.1016\/j.knosys.2025.113991","volume":"326","author":"L Zhenkun","year":"2025","unstructured":"Zhenkun L, Wei H, Ye F, Huang Q (2025) Biclustering-knn joint learning in anomaly detection for handling class-imbalance-problem. Knowl-Based Syst 326:113991","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"2767_CR12","doi-asserted-by":"publisher","first-page":"33208","DOI":"10.1038\/s41598-025-18326-x","volume":"15","author":"W Zheng","year":"2025","unstructured":"Zheng W, Wang J, Wang X (2025) Gene expression data mining by hybrid biclustering with improved GA and BA. Sci Rep 15(1):33208","journal-title":"Sci Rep"},{"key":"2767_CR13","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.patcog.2017.07.021","volume":"72","author":"M Denitto","year":"2017","unstructured":"Denitto M (2017) Spike and slab biclustering. Pattern Recogn 72:186\u2013195","journal-title":"Pattern Recogn"},{"key":"2767_CR14","doi-asserted-by":"publisher","first-page":"107318","DOI":"10.1016\/j.patcog.2020.107318","volume":"104","author":"M Denitto","year":"2020","unstructured":"Denitto M (2020) Biclustering with dominant sets. Pattern Recogn 104:107318","journal-title":"Pattern Recogn"},{"issue":"4","key":"2767_CR15","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1002\/sam.11581","volume":"15","author":"H Liu","year":"2022","unstructured":"Liu H, Zou J, Ravishanker N (2022) Biclustering high-frequency financial time series based on information theory. Stat Anal Data Min ASA Data Sci J 15(4):447\u2013462","journal-title":"Stat Anal Data Min ASA Data Sci J"},{"issue":"2","key":"2767_CR16","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1109\/TFUZZ.2019.2904920","volume":"28","author":"Q Huang","year":"2019","unstructured":"Huang Q, Yang J, Feng X, Liew AWC, Li X (2019) Automated trading point forecasting based on bicluster mining and fuzzy inference. IEEE Trans Fuzzy Syst 28(2):259\u2013272","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"2767_CR17","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/TFUZZ.2014.2327994","volume":"23","author":"L-X Wang","year":"2015","unstructured":"Wang L-X (2015) Dynamical models of stock prices based on technical trading rules part I: the models. IEEE Trans Fuzzy Syst 23(4):787\u2013801","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"2767_CR18","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1109\/TFUZZ.2014.2346244","volume":"23","author":"L-X Wang","year":"2015","unstructured":"Wang L-X (2015) Dynamical models of stock prices based on technical trading rules-part II: analysis of the model. IEEE Trans Fuzzy Syst 23(4):1127\u20131141","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"5","key":"2767_CR19","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1109\/TFUZZ.2014.2374193","volume":"23","author":"L-X Wang","year":"2015","unstructured":"Wang L-X (2015) Dynamical models of stock prices based on technical trading rules-part III: application to Hong Kong stocks. IEEE Trans Fuzzy Syst 23(5):1680\u20131697","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"2767_CR20","volume-title":"The analysis and interpretation of multivariate data for social scientists","author":"JI Galbraith","year":"2002","unstructured":"Galbraith JI, Moustaki I, Bartholomew DJ, Steele F (2002) The analysis and interpretation of multivariate data for social scientists. Chapman and Hall\/CRC, New York"},{"issue":"3","key":"2767_CR21","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/BF02289233","volume":"23","author":"HF Kaiser","year":"1958","unstructured":"Kaiser HF (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23(3):187\u2013200","journal-title":"Psychometrika"},{"issue":"8","key":"2767_CR22","doi-asserted-by":"publisher","first-page":"8219","DOI":"10.1007\/s10462-022-10366-3","volume":"56","author":"X Ran","year":"2023","unstructured":"Ran X, Xi Y, Yonggang L, Wang X, Zhenyu L (2023) Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artif Intell Rev 56(8):8219\u20138264","journal-title":"Artif Intell Rev"},{"key":"2767_CR23","unstructured":"Ester M, Kriegel HP, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining (KDD-96), pp. 226\u2013231"},{"key":"2767_CR24","unstructured":"MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth berkeley symposium, Vol 1, pp. 281\u2013297"},{"key":"2767_CR25","doi-asserted-by":"crossref","unstructured":"Reynolds DA (2009) Gaussian mixture models. In Encyclopedia of Biometrics, Springer, Berlin, pp. 659\u2013663","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"2767_CR26","unstructured":"Achelis SB (2000) Introduction. Trends - Technical Analysis from A to Z. Equis International, Denver, Colorado"},{"key":"2767_CR27","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.eswa.2017.12.026","volume":"97","author":"J Zhang","year":"2018","unstructured":"Zhang J, Cui S, Yan X, Li Q, Li T (2018) A novel data-driven stock price trend prediction system. Expert Syst Appl 97:60\u201369","journal-title":"Expert Syst Appl"},{"issue":"2","key":"2767_CR28","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.csda.2004.02.003","volume":"48","author":"H Turner","year":"2005","unstructured":"Turner H, Bailey T, Krzanowski W (2005) Improved biclustering of microarray data demonstrated through systematic performance tests. Comput Stat Data Anal 48(2):235\u2013254","journal-title":"Comput Stat Data Anal"},{"issue":"3","key":"2767_CR29","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1214\/09-AOAS239","volume":"3","author":"AA Shabalin","year":"2009","unstructured":"Shabalin AA, Weigman VJ, Perou CM, Nobel AB (2009) Finding large average submatrices in high dimensional data. Ann Appl Stat 3(3):985\u20131012","journal-title":"Ann Appl Stat"},{"issue":"337","key":"2767_CR30","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1080\/01621459.1972.10481214","volume":"67","author":"JA Hartigan","year":"1972","unstructured":"Hartigan JA (1972) Direct clustering of a data matrix. J Am Stat Assoc 67(337):123\u2013129","journal-title":"J Am Stat Assoc"},{"key":"2767_CR31","unstructured":"Yang J, Wang W, Wang H, Yu P(2002) $$\\delta $$-Clusters: capturing ubspace correlation in a large data set. In: Proceedings of the 18th international conference on data engineering"},{"issue":"5","key":"2767_CR32","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1142\/S0219720009004370","volume":"7","author":"A Mukhopadhyay","year":"2009","unstructured":"Mukhopadhyay A, Maulik U, Bandyopadhyay S (2009) A novel coherence measure for discovering scaling biclusters from gene expression data. Bioinform Comput Biol 7(5):853\u2013868","journal-title":"Bioinform Comput Biol"},{"issue":"11","key":"2767_CR33","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1109\/TKDE.2004.74","volume":"16","author":"KY Yip","year":"2004","unstructured":"Yip KY, Cheung DW, Michael KN (2004) HARP: a practical projected clustering algorithm. IEEE Trans Knowl Data Eng 16(11):1387\u20131397","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2767_CR34","doi-asserted-by":"crossref","unstructured":"Giraldez R, Divina F, Pontes B, Aguilar-Ruiz JS (2007) Evolutionary search of biclusters by minimal intrafluctuation. In IEEE International Fuzzy Systems Conference, IEEE","DOI":"10.1109\/FUZZY.2007.4295631"},{"issue":"2","key":"2767_CR35","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.compbiomed.2011.11.015","volume":"42","author":"F Divina","year":"2012","unstructured":"Divina F, Pontes B, Gir\u00e1ldez R, Aguilar-Ruiz JS (2012) An effective measure for assessing the quality of biclusters. Comput Biol Med 42(2):245\u2013256","journal-title":"Comput Biol Med"},{"issue":"3","key":"2767_CR36","doi-asserted-by":"publisher","first-page":"e0115497","DOI":"10.1371\/journal.pone.0115497","volume":"10","author":"B Pontes","year":"2015","unstructured":"Pontes B, Girldez R, Aguilar-Ruiz JS (2015) Quality measures for gene expression biclusters. PLoS ONE 10(3):e0115497","journal-title":"PLoS ONE"},{"key":"2767_CR37","doi-asserted-by":"crossref","unstructured":"Padilha VA, De Leon Ferreira\u00a0De Carvalho ACP (2019) Experimental correlation analysis of bicluster coherence measures and gene ontology information. Appl Soft Comput, 85:105688","DOI":"10.1016\/j.asoc.2019.105688"},{"key":"2767_CR38","unstructured":"Horton P, Nakai K (1996) A probabilistic classification system for predicting the cellular localization sites of proteins. In: Intelligent Systems in Molecular Biology, pp. 109\u2013115"},{"key":"2767_CR39","doi-asserted-by":"publisher","first-page":"7367","DOI":"10.1016\/j.eswa.2015.05.030","volume":"42","author":"Juan Gabriel Colonna","year":"2015","unstructured":"Juan Gabriel Colonna (2015) An incremental technique for real-time bioacoustic signal segmentation. Expert Syst Appl 42:7367\u20137374","journal-title":"Expert Syst Appl"},{"key":"2767_CR40","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.dss.2009.05.016","volume":"47","author":"P Cortez","year":"2009","unstructured":"Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 47:547\u2013553","journal-title":"Decis Support Syst"},{"issue":"3","key":"2767_CR41","doi-asserted-by":"publisher","first-page":"3489","DOI":"10.1007\/s11063-022-11019-w","volume":"55","author":"J Wang","year":"2023","unstructured":"Wang J, Jing F, He M (2023) Stock trading strategy of reinforcement learning driven by turning point classification. Neural Process Lett 55(3):3489\u20133508","journal-title":"Neural Process Lett"},{"issue":"10","key":"2767_CR42","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1109\/TCYB.2014.2370063","volume":"45","author":"Q Huang","year":"2015","unstructured":"Huang Q, Wang T, Tao D, Li X (2015) Biclustering learning of trading rules. IEEE Trans Cybern 45(10):2287\u20132298","journal-title":"IEEE Trans Cybern"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02767-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-026-02767-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02767-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:49:51Z","timestamp":1777006191000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-026-02767-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2767"],"URL":"https:\/\/doi.org\/10.1007\/s10115-026-02767-5","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"27 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":4,"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"}}],"article-number":"134"}}