{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T13:03:51Z","timestamp":1786280631830,"version":"build-2736575974"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T00:00:00Z","timestamp":1563408000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T00:00:00Z","timestamp":1563408000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10479-019-03284-1","type":"journal-article","created":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T19:02:33Z","timestamp":1563476553000},"page":"1379-1395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Trimmed fuzzy clustering of financial time series based on dynamic time warping"],"prefix":"10.1007","volume":"299","author":[{"given":"Pierpaolo","family":"D\u2019Urso","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Livia","family":"De Giovanni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Massari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,7,18]]},"reference":[{"issue":"5","key":"3284_CR1","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/TFUZZ.2010.2052258","volume":"18","author":"DT Anderson","year":"2010","unstructured":"Anderson, D. T., Bezdek, J. C., Popescu, M., & Keller, J. M. (2010). Comparing fuzzy, probabilistic, and possibilistic partitions. IEEE Transactions on Fuzzy Systems, 18(5), 906\u2013918.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"519","key":"3284_CR2","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1080\/01621459.2016.1195743","volume":"112","author":"T Ando","year":"2017","unstructured":"Ando, T., & Bai, J. (2017). Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures. Journal of the American Statistical Association, 112(519), 1182\u20131198.","journal-title":"Journal of the American Statistical Association"},{"issue":"1\u20132","key":"3284_CR3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10479-017-2659-0","volume":"260","author":"S Aslan","year":"2018","unstructured":"Aslan, S., Yozgatligil, C., & Iyigun, C. (2018). Temporal clustering of time series via threshold autoregressive models: Application to commodity prices. Annals of Operations Research, 260(1\u20132), 51\u201377.","journal-title":"Annals of Operations Research"},{"issue":"2","key":"3284_CR4","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.physa.2007.01.011","volume":"379","author":"N Basalto","year":"2007","unstructured":"Basalto, N., Bellotti, R., De Carlo, F., Facchi, P., Pantaleo, E., & Pascazio, S. (2007). Hausdorff clustering of financial time series. Physica A: Statistical Mechanics and its Applications, 379(2), 635\u2013644.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"4","key":"3284_CR5","doi-asserted-by":"crossref","first-page":"046112","DOI":"10.1103\/PhysRevE.78.046112","volume":"78","author":"N Basalto","year":"2008","unstructured":"Basalto, N., Bellotti, R., De Carlo, F., Facchi, P., Pantaleo, E., & Pascazio, S. (2008). Hausdorff clustering. Physical Review E, 78(4), 046112.","journal-title":"Physical Review E"},{"issue":"12","key":"3284_CR6","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1080\/14697688.2012.726736","volume":"14","author":"JA Bastos","year":"2014","unstructured":"Bastos, J. A., & Caiado, J. (2014). Clustering financial time series with variance ratio statistics. Quantitative Finance, 14(12), 2121\u20132133.","journal-title":"Quantitative Finance"},{"key":"3284_CR7","unstructured":"Berndt, D.J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Proceedings of the AAAI-94 workshop knowledge discovery in databases (pp. 359\u2013370). Seattle, WA."},{"key":"3284_CR8","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1142\/9789812709691_0064","volume-title":"Recent Advances in Stochastic Modeling and Data Analysis","author":"J Caiado","year":"2007","unstructured":"Caiado, J., & Crato, N. (2007). A GARCH-based method for clustering of financial time series: International stock markets evidence. In C. Skiadas (Ed.), Recent Advances in Stochastic Modeling and Data Analysis (pp. 542\u2013551). Singapore: World Scientific."},{"key":"3284_CR9","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1016\/j.fss.2006.07.006","volume":"157","author":"RJGB Campello","year":"2006","unstructured":"Campello, R. J. G. B., & Hruschka, E. R. (2006). A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets and Systems, 157, 2858\u20132875.","journal-title":"Fuzzy Sets and Systems"},{"key":"3284_CR10","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.intfin.2017.08.004","volume":"52","author":"S-L Chang","year":"2018","unstructured":"Chang, S.-L., Chien, C.-Y., Lee, H.-C., & Lin, C. (2018). Historical high and stock index returns: Application of the regression kink model. Journal of International Financial Markets, Institutions and Money, 52, 48\u201363.","journal-title":"Journal of International Financial Markets, Institutions and Money"},{"issue":"2","key":"3284_CR11","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/91.580801","volume":"5","author":"RN Dav\u00e9","year":"1997","unstructured":"Dav\u00e9, R. N., & Krishnapuram, R. (1997). Robust clustering methods: A unified view. IEEE Transactions on Fuzzy Systems, 5(2), 270\u2013293.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"2","key":"3284_CR12","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.csda.2009.10.005","volume":"54","author":"A De Gregorio","year":"2010","unstructured":"De Gregorio, A., & Iacus, S. M. (2010). Clustering of discretely observed diffusion processes. Computational Statistics & Data Analysis, 54(2), 598\u2013606.","journal-title":"Computational Statistics & Data Analysis"},{"issue":"4","key":"3284_CR13","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s11634-011-0098-3","volume":"5","author":"G De Luca","year":"2011","unstructured":"De Luca, G., & Zuccolotto, P. (2011). A tail dependence-based dissimilarity measure for financial time series clustering. Advances in Data Analysis and Classification, 5(4), 323\u2013340.","journal-title":"Advances in Data Analysis and Classification"},{"issue":"1\u20132","key":"3284_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1515\/strm-2015-0026","volume":"34","author":"G De Luca","year":"2017","unstructured":"De Luca, G., & Zuccolotto, P. (2017). A double clustering algorithm for financial time series based on extreme events. Statistics & Risk Modeling, 34(1\u20132), 1\u201312.","journal-title":"Statistics & Risk Modeling"},{"key":"3284_CR15","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.gfj.2015.05.002","volume":"29","author":"S Degiannakis","year":"2016","unstructured":"Degiannakis, S., & Floros, C. (2016). Intra-day realized volatility for European and USA stock indices. Global Finance Journal, 29, 24\u201341.","journal-title":"Global Finance Journal"},{"issue":"3","key":"3284_CR16","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.ejor.2014.12.041","volume":"243","author":"JG Dias","year":"2015","unstructured":"Dias, J. G., Vermunt, J. K., & Ramos, S. (2015). Clustering financial time series: New insights from an extended hidden Markov model. European Journal of Operational Research, 243(3), 852\u2013864.","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"3284_CR17","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.physa.2005.02.078","volume":"355","author":"C Dose","year":"2005","unstructured":"Dose, C., & Cincotti, S. (2005). Clustering of financial time series with application to index and enhanced index tracking portfolio. Physica A: Statistical Mechanics and its Applications, 355(1), 145\u2013151.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"4","key":"3284_CR18","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11634-013-0160-4","volume":"8","author":"F Durante","year":"2014","unstructured":"Durante, F., Pappad\u00e0, R., & Torelli, N. (2014). Clustering of financial time series in risky scenarios. Advances in Data Analysis and Classification, 8(4), 359\u2013376.","journal-title":"Advances in Data Analysis and Classification"},{"issue":"1\u20133","key":"3284_CR19","first-page":"53","volume":"9","author":"P D\u2019Urso","year":"2000","unstructured":"D\u2019Urso, P. (2000). Dissimilarity measures for time trajectories. Statistical Methods & Applications, 9(1\u20133), 53\u201383.","journal-title":"Statistical Methods & Applications"},{"issue":"03","key":"3284_CR20","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1142\/S0218488504002849","volume":"12","author":"P D\u2019Urso","year":"2004","unstructured":"D\u2019Urso, P. (2004). Fuzzy C-Means clustering models for multivariate time-varying data: Different approaches. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12(03), 287\u2013326.","journal-title":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"},{"issue":"5","key":"3284_CR21","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TFUZZ.2005.856565","volume":"13","author":"P D\u2019Urso","year":"2005","unstructured":"D\u2019Urso, P. (2005). Fuzzy clustering for data time arrays with inlier and outlier time trajectories. IEEE Transactions on Fuzzy Systems, 13(5), 583\u2013604.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"9","key":"3284_CR22","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1016\/j.physa.2013.01.027","volume":"392","author":"P D\u2019Urso","year":"2013","unstructured":"D\u2019Urso, P., Cappelli, C., Di Lallo, D., & Massari, R. (2013). Clustering of financial time series. Physica A: Statistical Mechanics and its Applications, 392(9), 2114\u20132129.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"3284_CR23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fss.2016.01.010","volume":"305","author":"P D\u2019Urso","year":"2016","unstructured":"D\u2019Urso, P., De Giovanni, L., & Massari, R. (2016). GARCH-based robust clustering of time series. Fuzzy Sets and Systems, 305, 1\u201328.","journal-title":"Fuzzy Sets and Systems"},{"key":"3284_CR24","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.ijar.2018.05.002","volume":"99","author":"P D\u2019Urso","year":"2018","unstructured":"D\u2019Urso, P., De Giovanni, L., & Massari, R. (2018). Robust fuzzy clustering of multivariate time trajectories. International Journal of Approximate Reasoning, 99, 12\u201338.","journal-title":"International Journal of Approximate Reasoning"},{"key":"3284_CR25","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.chemolab.2016.11.016","volume":"161","author":"P D\u2019Urso","year":"2017","unstructured":"D\u2019Urso, P., Massari, R., Cappelli, C., & De Giovanni, L. (2017). Autoregressive metric-based trimmed fuzzy clustering with an application to $$\\text{ PM }_{10}$$ time series. Chemometrics and Intelligent Laboratory Systems, 161, 15\u201326.","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"3284_CR26","first-page":"956","volume":"94","author":"L\u00c1 Garc\u00eda-Escudero","year":"1999","unstructured":"Garc\u00eda-Escudero, L. \u00c1., & Gordaliza, A. (1999). Robustness properties of k means and trimmed k means. Journal of the American Statistical Association, 94, 956\u2013969.","journal-title":"Journal of the American Statistical Association"},{"key":"3284_CR27","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1198\/1061860031806","volume":"12","author":"LA Garc\u00eda-Escudero","year":"2003","unstructured":"Garc\u00eda-Escudero, L. A., Gordaliza, A., & Matr\u00e1n, C. (2003). Trimming tools in exploratory data analysis. Journal of Computational and Graphical Statistics, 12, 434\u2013449.","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"3284_CR28","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s11634-010-0064-5","volume":"4","author":"LA Garc\u00eda-Escudero","year":"2010","unstructured":"Garc\u00eda-Escudero, L. A., Gordaliza, A., Matr\u00e1n, C., & Mayo-Iscar, A. (2010). A review of robust clustering methods. Advances in Data Analysis and Classification, 4, 89\u2013109.","journal-title":"Advances in Data Analysis and Classification"},{"issue":"7","key":"3284_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v031.i07","volume":"31","author":"T Giorgino","year":"2009","unstructured":"Giorgino, T., et al. (2009). Computing and visualizing dynamic time warping alignments in R: The dtw package. Journal of Statistical Software, 31(7), 1\u201324.","journal-title":"Journal of Statistical Software"},{"issue":"6","key":"3284_CR30","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1016\/j.jmva.2007.07.002","volume":"99","author":"C Hennig","year":"2008","unstructured":"Hennig, C., et al. (2008). Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods. Journal of Multivariate Analysis, 99(6), 1154\u20131176.","journal-title":"Journal of Multivariate Analysis"},{"key":"3284_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.econmod.2015.06.004","volume":"50","author":"EM Iglesias","year":"2015","unstructured":"Iglesias, E. M. (2015). Value at Risk and expected shortfall of firms in the main European Union stock market indexes: A detailed analysis by economic sectors and geographical situation. Economic Modelling, 50, 1\u20138.","journal-title":"Economic Modelling"},{"key":"3284_CR32","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.engappai.2014.12.015","volume":"39","author":"H Izakian","year":"2015","unstructured":"Izakian, H., Pedrycz, W., & Jamal, I. (2015). Fuzzy clustering of time series data using dynamic time warping distance. Engineering Applications of Artificial Intelligence, 39, 235\u2013244.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"3284_CR33","unstructured":"Kamdar, T., & Joshi, A. (2000). On creating adaptive Web servers using Weblog Mining. Technical report TR-CS- 00-05, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County."},{"key":"3284_CR34","doi-asserted-by":"crossref","unstructured":"Lafuente-Rego, B., D\u2019Urso, P., & Vilar, J. (in press 2019). Robust fuzzy clustering based on quantile autocovariances. Statistical Papers.","DOI":"10.1007\/s00362-018-1053-6"},{"issue":"2","key":"3284_CR35","doi-asserted-by":"crossref","first-page":"3761","DOI":"10.1016\/j.eswa.2008.02.025","volume":"36","author":"RK Lai","year":"2009","unstructured":"Lai, R. K., Fan, C.-Y., Huang, W.-H., & Chang, P.-C. (2009). Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems with Applications, 36(2), 3761\u20133773.","journal-title":"Expert Systems with Applications"},{"key":"3284_CR36","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.iref.2017.01.005","volume":"48","author":"Q Liu","year":"2017","unstructured":"Liu, Q., & Tse, Y. (2017). Overnight returns of stock indexes: Evidence from ETFs and futures. International Review of Economics & Finance, 48, 440\u2013451.","journal-title":"International Review of Economics & Finance"},{"key":"3284_CR37","doi-asserted-by":"crossref","DOI":"10.1201\/9780429058264","volume-title":"Time series clustering and classification","author":"EA Maharaj","year":"2019","unstructured":"Maharaj, E. A., D\u2019Urso, P., & Caiado, J. (2019). Time series clustering and classification. Boca Raton: CRC Press."},{"issue":"2","key":"3284_CR38","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s00357-010-9058-4","volume":"27","author":"EA Maharaj","year":"2010","unstructured":"Maharaj, E. A., D\u2019Urso, P., & Galagedera, D. U. (2010). Wavelet-based fuzzy clustering of time series. Journal of Classification, 27(2), 231\u2013275.","journal-title":"Journal of Classification"},{"issue":"1\u20134","key":"3284_CR39","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0168-1923(85)90082-6","volume":"35","author":"A McBratney","year":"1985","unstructured":"McBratney, A., & Moore, A. (1985). Application of fuzzy sets to climatic classification. Agricultural and Forest Meteorology, 35(1\u20134), 165\u2013185.","journal-title":"Agricultural and Forest Meteorology"},{"issue":"1","key":"3284_CR40","first-page":"44","volume":"8","author":"G Menardi","year":"2015","unstructured":"Menardi, G., & Lisi, F. (2015). Double clustering for rating mutual funds. Electronic Journal of Applied Statistical Analysis, 8(1), 44\u201356.","journal-title":"Electronic Journal of Applied Statistical Analysis"},{"key":"3284_CR41","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.eswa.2016.11.002","volume":"70","author":"BB Nair","year":"2017","unstructured":"Nair, B. B., Kumar, P. S., Sakthivel, N., & Vipin, U. (2017). Clustering stock price time series data to generate stock trading recommendations: An empirical study. Expert Systems with Applications, 70, 20\u201336.","journal-title":"Expert Systems with Applications"},{"key":"3284_CR42","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/ecj.12140","volume":"102","author":"K Nakagawa","year":"2019","unstructured":"Nakagawa, K., Imamura, M., & Yoshida, K. (2019). Stock price prediction using k-medoids clustering with indexing dynamic time warping. Electronics and Communications in Japan, 102, 3\u20138.","journal-title":"Electronics and Communications in Japan"},{"issue":"1","key":"3284_CR43","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ecoinf.2005.10.006","volume":"1","author":"F Okeke","year":"2006","unstructured":"Okeke, F., & Karnieli, A. (2006). Linear mixture model approach for selecting fuzzy exponent value in fuzzy c-means algorithm. Ecological Informatics, 1(1), 117\u2013124.","journal-title":"Ecological Informatics"},{"issue":"2","key":"3284_CR44","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.csda.2003.11.009","volume":"47","author":"F Pattarin","year":"2004","unstructured":"Pattarin, F., Paterlini, S., & Minerva, T. (2004). Clustering financial time series: An application to mutual funds style analysis. Computational Statistics & Data Analysis, 47(2), 353\u2013372.","journal-title":"Computational Statistics & Data Analysis"},{"issue":"01","key":"3284_CR45","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1142\/S012918311101604X","volume":"22","author":"C Piccardi","year":"2011","unstructured":"Piccardi, C., Calatroni, L., & Bertoni, F. (2011). Clustering financial time series by network community analysis. International Journal of Modern Physics C, 22(01), 35\u201350.","journal-title":"International Journal of Modern Physics C"},{"issue":"2","key":"3284_CR46","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1007\/s00357-018-9258-x","volume":"35","author":"J Rahmanishamsi","year":"2018","unstructured":"Rahmanishamsi, J., Dolati, A., & Aghabozorgi, M. R. (2018). A copula based ICA algorithm and its application to time series clustering. Journal of Classification, 35(2), 230\u2013249.","journal-title":"Journal of Classification"},{"key":"3284_CR47","unstructured":"Ratanamahatana, C. A., & Keogh, E. (2004). Everything you know about dynamic time warping is wrong. In Third workshop on mining temporal and sequential data. Citeseer."},{"issue":"3\u20134","key":"3284_CR48","doi-asserted-by":"crossref","first-page":"169","DOI":"10.3233\/AF-13025","volume":"2","author":"M Rechenthin","year":"2013","unstructured":"Rechenthin, M., Street, W. N., & Srinivasan, P. (2013). Stock chatter: Using stock sentiment to predict price direction. Algorithmic Finance, 2(3\u20134), 169\u2013196.","journal-title":"Algorithmic Finance"},{"issue":"3","key":"3284_CR49","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0020-7373(70)80008-6","volume":"2","author":"V Velichko","year":"1970","unstructured":"Velichko, V., & Zagoruyko, N. (1970). Automatic recognition of 200 words. International Journal of Man-Machine Studies, 2(3), 223\u2013234.","journal-title":"International Journal of Man-Machine Studies"},{"key":"3284_CR50","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.fss.2017.03.006","volume":"340","author":"JA Vilar","year":"2018","unstructured":"Vilar, J. A., Lafuente-Rego, B., & D\u2019Urso, P. (2018). Quantile autocovariances: A powerful tool for hard and soft partitional clustering of time series. Fuzzy Sets and Systems, 340, 38\u201372.","journal-title":"Fuzzy Sets and Systems"},{"issue":"1","key":"3284_CR51","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s00357-009-9030-3","volume":"26","author":"JM Vilar","year":"2009","unstructured":"Vilar, J. M., Vilar, J. A., & P\u00e9rtega, S. (2009). Classifying time series data: A nonparametric approach. Journal of classification, 26(1), 3\u201328.","journal-title":"Journal of classification"},{"issue":"4","key":"3284_CR52","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/0167-8116(89)90052-9","volume":"6","author":"M Wedel","year":"1989","unstructured":"Wedel, M., & Steenkamp, J. (1989). A fuzzy clusterwise regression approach to benefit segmentation. International Journal of Research in Marketing, 6(4), 241\u2013258.","journal-title":"International Journal of Research in Marketing"},{"issue":"8","key":"3284_CR53","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/34.85677","volume":"13","author":"XL Xie","year":"1991","unstructured":"Xie, X. L., & Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(8), 841\u2013847.","journal-title":"IEEE Transactions on Pattern Analysis & Machine Intelligence"},{"issue":"3","key":"3284_CR54","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s10260-017-0411-1","volume":"27","author":"C Yang","year":"2018","unstructured":"Yang, C., Jiang, W., Wu, J., Liu, X., & Li, Z. (2018). Clustering of financial instruments using jump tail dependence coefficient. Statistical Methods & Applications, 27(3), 491\u2013513.","journal-title":"Statistical Methods & Applications"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-019-03284-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10479-019-03284-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-019-03284-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T16:19:42Z","timestamp":1617380382000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10479-019-03284-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,18]]},"references-count":54,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["3284"],"URL":"https:\/\/doi.org\/10.1007\/s10479-019-03284-1","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,18]]},"assertion":[{"value":"18 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}