{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:02:13Z","timestamp":1740153733522,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T00:00:00Z","timestamp":1552003200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571193"],"award-info":[{"award-number":["61571193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["2017A030312006"],"award-info":[{"award-number":["2017A030312006"]}]},{"name":"Science and Technology Program of Guangzhou","award":["201704020134"],"award-info":[{"award-number":["201704020134"]}]},{"name":"Project\u00a0of\u00a0Science and Technology Department of Guangdong province","award":["2016A010101021, 2016A010101022 and\u00a02016A010101023"],"award-info":[{"award-number":["2016A010101021, 2016A010101022 and\u00a02016A010101023"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s12559-019-9626-9","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T03:16:13Z","timestamp":1552014973000},"page":"799-808","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Determination of Temporal Stock Investment Styles via Biclustering Trading Patterns"],"prefix":"10.1007","volume":"11","author":[{"given":"Jianjun","family":"Sun","sequence":"first","affiliation":[]},{"given":"Qinghua","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xuelong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,8]]},"reference":[{"issue":"12","key":"9626_CR1","doi-asserted-by":"publisher","first-page":"3193","DOI":"10.1007\/s00500-015-2003-z","volume":"21","author":"F Wang","year":"2017","unstructured":"Wang F, Zhang Y, Rao Q, Li K, Zhang H. Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction. Soft Comput. 2017;21(12):3193\u2013205.","journal-title":"Soft Comput"},{"issue":"2","key":"9626_CR2","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1016\/j.eswa.2006.10.028","volume":"34","author":"T Chavarnakul","year":"2008","unstructured":"Chavarnakul T, Enke D. Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Syst Appl. 2008;34(2):1004\u201317.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"9626_CR3","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/TSMCC.2008.2007255","volume":"39","author":"PC Chang","year":"2009","unstructured":"Chang PC, Fan CY, Liu CH. Integrating a piecewise linear representation method and a neural network model for stock trading points prediction. IEEE Trans Syst Man Cybern Part C Appl Rev. 2009;39(1):80\u201392.","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"key":"9626_CR4","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.ins.2013.12.022","volume":"281","author":"Y Li","year":"2014","unstructured":"Li Y, Liu W, Li X, Huang Q, Li X. GA-SIFT: a new scale invariant feature transform for multispectral image using geometric algebra. Inf Sci. 2014;281:559\u201372.","journal-title":"Inf Sci"},{"issue":"5","key":"9626_CR5","doi-asserted-by":"publisher","first-page":"2487","DOI":"10.1007\/s11042-015-2637-y","volume":"75","author":"Y Li","year":"2016","unstructured":"Li Y, Liu W, Huang Q. Traffic anomaly detection based on image descriptor in videos. Multimed Tools Appl. 2016;75(5):2487\u2013505.","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"9626_CR6","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/s11135-008-9176-9","volume":"43","author":"YH Wang","year":"2009","unstructured":"Wang YH. Using neural network to forecast stock index option price: a new hybrid GARCH approach. Qual Quant. 2009;43(5):833\u201343.","journal-title":"Qual Quant"},{"key":"9626_CR7","unstructured":"Tilakaratne CD, Morris SA, Mammadov MA, Hurst C P. Predicting stock market index trading signals using neural networks. In: Proceedings of the 14th annual global finance conference (GFC\u201907); 2007. pp. 171-179."},{"key":"9626_CR8","doi-asserted-by":"crossref","unstructured":"White H. Economic prediction using neural networks: the case of IBM daily stock returns. In: IEEE International Conference on Neural Networks; 1988. vol. 2, pp451\u2013458.","DOI":"10.1109\/ICNN.1988.23959"},{"issue":"4","key":"9626_CR9","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/2785970","volume":"10","author":"Y Wu","year":"2016","unstructured":"Wu Y, Zhu X, Li L, et al. Mining dual networks: models, algorithms, and applications. ACM Trans Knowl Discov Data. 2016;10(4):40.","journal-title":"ACM Trans Knowl Discov Data"},{"key":"9626_CR10","unstructured":"Zhang D, Jiang Q, and Li X (2004) Application of neural networks in financial data mining. In: International Conference on Computational Intelligence, pp. 392-395."},{"issue":"3","key":"9626_CR11","first-page":"43","volume":"8","author":"S Zhang","year":"2017","unstructured":"Zhang S, Li X, Zong M, et al. Learning k for knn classification. ACM Trans Intell Syst Technol. 2017;8(3):43.","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"7","key":"9626_CR12","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1016\/S0305-0548(03)00063-7","volume":"31","author":"JY Potvin","year":"2004","unstructured":"Potvin JY, Soriano P, Vall\u00e9e M. Generating trading rules on the stock markets with genetic programming. Comput Oper Res. 2004;31(7):1033\u20131047.4.","journal-title":"Comput Oper Res"},{"issue":"1","key":"9626_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11156-017-0621-5","volume":"50","author":"JE Hilliard","year":"2018","unstructured":"Hilliard JE, Hilliard, J. Rebalancing versus buy and hold: theory, simulation and empirical analysis. Rev Quant Finan Acc. 2018;50(1):1\u201332.","journal-title":"Rev Quant Finan Acc"},{"issue":"1","key":"9626_CR14","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.eswa.2006.08.020","volume":"34","author":"PC Chang","year":"2008","unstructured":"Chang PC, Liu CH. A TSK type fuzzy rule based system for stock price prediction. Expert Syst Appl. 2008;34(1):135\u201344.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"9626_CR15","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1016\/j.asoc.2010.02.017","volume":"11","author":"AC Briza","year":"2011","unstructured":"Briza AC, Naval PC Jr. Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Appl Soft Comput. 2011;11(1):1191\u2013201.","journal-title":"Appl Soft Comput"},{"issue":"2","key":"9626_CR16","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/S0957-4174(02)00034-9","volume":"23","author":"W Leigh","year":"2002","unstructured":"Leigh W, Modani N, Purvis R, Roberts T. Stock market trading rule discovery using technical charting heuristics. Expert Syst Appl. 2002;23(2):155\u20139.","journal-title":"Expert Syst Appl"},{"issue":"10","key":"9626_CR17","doi-asserted-by":"publisher","first-page":"2513","DOI":"10.1016\/j.cor.2004.03.016","volume":"32","author":"W Huang","year":"2005","unstructured":"Huang W, Nakamori Y, Wang SY. Forecasting stock market movement direction with support vector machine. Comput Oper Res. 2005;32(10):2513\u201322.","journal-title":"Comput Oper Res"},{"key":"9626_CR18","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.neucom.2014.08.104","volume":"172","author":"F Yang","year":"2016","unstructured":"Yang F, Huang Q, Jin L, Wee-Chung Liew A. Segmentation and recognition of multi-model photo event. Neurocomputing. 2016;172:159\u201367.","journal-title":"Neurocomputing"},{"issue":"1","key":"9626_CR19","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.eswa.2007.09.034","volume":"36","author":"MHF Zarandi","year":"2009","unstructured":"Zarandi MHF, Rezaee B, Turksen IB, et al. A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Syst Appl. 2009;36(1):139\u201354.","journal-title":"Expert Syst Appl"},{"key":"9626_CR20","first-page":"501","volume-title":"European conference on machine learning","author":"H Zhang","year":"2004","unstructured":"Zhang H, Su J. Naive bayesian classifiers for ranking. In: European conference on machine learning. Berlin: Springer; 2004. p. 501\u201312."},{"issue":"3","key":"9626_CR21","first-page":"16","volume":"3","author":"A Upadhyay","year":"2012","unstructured":"Upadhyay A, Bandyopadhyay G, Dutta A. Forecasting stock performance in Indian market using multinomial logistic regression. J Bus Stud Q. 2012;3(3):16.","journal-title":"J Bus Stud Q"},{"issue":"6","key":"9626_CR22","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1007\/s12559-017-9503-3","volume":"9","author":"MZ Asghar","year":"2017","unstructured":"Asghar MZ, Khan A, Bibi A, Kundi FM, Ahmad H. Sentence-level emotion detection framework using rule-based classification. Cogn Comput. 2017;9(6):868\u201394.","journal-title":"Cogn Comput"},{"issue":"3","key":"9626_CR23","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s12559-017-9539-4","volume":"10","author":"X Sun","year":"2018","unstructured":"Sun X, Peng X, Ding S. Emotional human-machine conversation generation based on long short-term memory. Cogn Comput. 2018;10(3):389\u201397.","journal-title":"Cogn Comput"},{"issue":"1","key":"9626_CR24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s12559-015-9342-z","volume":"8","author":"S Ding","year":"2016","unstructured":"Ding S, Zhang J, Jia H, Qian J. An adaptive density data stream clustering algorithm. Cogn Comput. 2016;8(1):30\u20138.","journal-title":"Cogn Comput"},{"issue":"10","key":"9626_CR25","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. Biclustering learning of trading rules. IEEE Trans Cybern. 2015;45(10):2287\u201398.","journal-title":"IEEE Trans Cybern"},{"key":"9626_CR26","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1016\/j.neucom.2017.10.010","volume":"275","author":"A Hussain","year":"2018","unstructured":"Hussain A, Cambria E. Semi-supervised learning for big social data analysis. Neurocomputing. 2018;275:1662\u201373.","journal-title":"Neurocomputing"},{"key":"9626_CR27","first-page":"1","volume":"9","author":"M Mahmud","year":"2018","unstructured":"Mahmud M, Kaiser MS, Rahman MM, Rahman MA, Shabut AM, Mamun SA, et al. A brain-inspired trust management model to assure security in a cloud based iot framework for neuroscience applications. Cogn Comput. 2018;9:1\u201310.","journal-title":"Cogn Comput"},{"issue":"1","key":"9626_CR28","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s00521-016-2647-3","volume":"30","author":"ZK Malik","year":"2018","unstructured":"Malik ZK, Hussain A, Wu QMJ. Extracting online information from dual and multiple data streams. Neural Comput & Applic. 2018;30(1):87\u201398.","journal-title":"Neural Comput & Applic"},{"issue":"7","key":"9626_CR29","doi-asserted-by":"publisher","first-page":"3249","DOI":"10.1109\/TIP.2016.2563981","volume":"25","author":"C Gong","year":"2016","unstructured":"Gong C, Tao D, Maybank SJ, Liu W, Kang G, Yang J. Multi-modal curriculum learning for semi-supervised image classification. IEEE Trans Image Process. 2016;25(7):3249\u201360.","journal-title":"IEEE Trans Image Process"},{"issue":"10","key":"9626_CR30","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1109\/TNNLS.2014.2376936","volume":"26","author":"C Gong","year":"2015","unstructured":"Gong C, Liu T, Tao D, Fu K, Tu E, Yang J. Deformed graph Laplacian for semisupervised learning. IEEE Trans Neural Netw Learn Syst. 2015;26(10):2261\u201374.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"9626_CR31","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1109\/TCYB.2017.2669639","volume":"48","author":"C Gong","year":"2018","unstructured":"Gong C, Liu T, Tang Y, Yang J, Yang J, Tao D. A regularization approach for instance-based superset label learning. IEEE Trans Cybern. 2018;48(3):967\u201378.","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"9626_CR32","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.gene.2012.11.085","volume":"518","author":"M Wang","year":"2013","unstructured":"Wang M, Shang X, Li X, Liu W, Li Z. Efficient mining differential co-expression biclusters in microarray datasets. Gene. 2013;518(1):59\u201369.","journal-title":"Gene"},{"key":"9626_CR33","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/978-3-642-23214-5_3","volume":"224","author":"Q Huang","year":"2011","unstructured":"Huang Q. A biclustering technique for mining trading rules in stock markets. Appl Inform Commun. 2011;224:16\u201324.","journal-title":"Appl Inform Commun"},{"issue":"5","key":"9626_CR34","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1109\/TEVC.2018.2884521","volume":"23","author":"Qinghua Huang","year":"2019","unstructured":"Huang Q, Huang X, Kong Z, Li X, Tao D. Bi-phase evolutionary searching for biclusters in gene expression data. IEEE Trans Evol Comput. 2018. \nhttps:\/\/doi.org\/10.1109\/TEVC.2018.2884521\n\n.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"9626_CR35","doi-asserted-by":"publisher","unstructured":"Huang Q, Chen Y, Liu L, Tao D, Li X. On combining biclustering mining and AdaBoost for breast tumor classification. IEEE Trans Evol Comput. 2019. \nhttps:\/\/doi.org\/10.1109\/TKDE.2019.2891622\n\n.","DOI":"10.1109\/TKDE.2019.2891622"},{"issue":"6","key":"9626_CR36","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.1007\/s11280-018-0534-9","volume":"21","author":"Q Huang","year":"2018","unstructured":"Huang Q, Kong Z, Li Y, Yang J, Li X. Discovery of trading points based on Bayesian modeling of trading rules. World Wide Web. 2018;21(6):1473\u201390.","journal-title":"World Wide Web"},{"issue":"12","key":"9626_CR37","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1016\/S0378-4266(99)00042-4","volume":"23","author":"M Ratner","year":"1999","unstructured":"Ratner M, Leal RPC. Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. J Bank Financ. 1999;23(12):1887\u2013905.","journal-title":"J Bank Financ"},{"key":"9626_CR38","unstructured":"Xu C, Tao D, Xu C. A survey on multi-view learning. Comput Sci. 2013."},{"issue":"10","key":"9626_CR39","doi-asserted-by":"publisher","first-page":"3102","DOI":"10.1016\/j.patcog.2014.12.016","volume":"48","author":"L Zhang","year":"2015","unstructured":"Zhang L, Zhang Q, Zhang L, Tao D, Huang X, Du B. Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recogn. 2015;48(10):3102\u201312.","journal-title":"Pattern Recogn"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-019-9626-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12559-019-9626-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-019-9626-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,7]],"date-time":"2020-03-07T00:09:02Z","timestamp":1583539742000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12559-019-9626-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,8]]},"references-count":39,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["9626"],"URL":"https:\/\/doi.org\/10.1007\/s12559-019-9626-9","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2019,3,8]]},"assertion":[{"value":"19 June 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"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"}}]}}