{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:47:18Z","timestamp":1754156838759,"version":"3.41.2"},"reference-count":50,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2023,11,15]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>First, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Finding<\/jats:title><jats:p>According to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-08-2022-0300","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:44:49Z","timestamp":1678927489000},"page":"756-779","source":"Crossref","is-referenced-by-count":1,"title":["A new method based on ensemble time series for fast and accurate clustering"],"prefix":"10.1108","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4034-8250","authenticated-orcid":false,"given":"Ali","family":"Ghorbanian","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1837-7933","authenticated-orcid":false,"given":"Hamideh","family":"Razavi","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"issue":"2","key":"key2023111509330289100_ref001","first-page":"221","article-title":"A new methodology for customer behavior analysis using time series clustering: a case study on a bank's customers","volume":"50","year":"2019","journal-title":"Kybernetes"},{"key":"key2023111509330289100_ref002","first-page":"562194","article-title":"A hybrid algorithm for clustering of time series data based on affinity search technique","volume":"2014","year":"2014","journal-title":"The Scientific World Journal"},{"key":"key2023111509330289100_ref003","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13040-017-0156-2","article-title":"Cluster ensemble based on Random Forests for genetic data","volume":"10","year":"2017","journal-title":"BioData Mining"},{"issue":"5","key":"key2023111509330289100_ref004","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1002\/wics.1270","article-title":"Semi\u2010supervised clustering methods","volume":"5","year":"2013","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"key2023111509330289100_ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/modelling1010001","article-title":"Time series clustering: a complex network-based approach for feature selection in multi-sensor data","volume":"1","year":"2020","journal-title":"Modelling"},{"key":"key2023111509330289100_ref006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cosrev.2018.01.003","article-title":"Cluster ensembles: a survey of approaches with recent extensions and applications","volume":"28","year":"2018","journal-title":"Computer Science Review"},{"key":"key2023111509330289100_ref007","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.ins.2021.12.001","article-title":"A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting","volume":"586","year":"2022","journal-title":"Information Sciences"},{"first-page":"237","article-title":"Fast time-series searching with scaling and shifting","year":"1999","key":"key2023111509330289100_ref008"},{"key":"key2023111509330289100_ref009","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s10479-019-03284-1","article-title":"Trimmed fuzzy clustering of financial time series based on dynamic time warping","volume":"299","year":"2021","journal-title":"Annals of Operations Research"},{"key":"key2023111509330289100_ref010","unstructured":"Da Silva, V.A.F. (2018), \u201cTime series analysis based on complex networks\u201d, PhD Dissertation, Universidade do Porto, Portugal."},{"key":"key2023111509330289100_ref011","doi-asserted-by":"crossref","first-page":"8591","DOI":"10.1007\/s13369-020-04753-7","article-title":"Classification of uncontrolled intersections using hierarchical clustering","volume":"45","year":"2020","journal-title":"Arabian Journal for Science and Engineering"},{"key":"key2023111509330289100_ref012","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","article-title":"The UCR time series archive","volume":"6","year":"2019","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"first-page":"999","article-title":"Semi-supervision dramatically improves time series clustering under dynamic time warping","year":"2016","key":"key2023111509330289100_ref013"},{"key":"key2023111509330289100_ref014","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"The Journal of Machine Learning Research"},{"key":"key2023111509330289100_ref015","doi-asserted-by":"crossref","first-page":"106499","DOI":"10.1016\/j.asoc.2020.106499","article-title":"Finding longest common subsequences: new anytime A* search results","volume":"95","year":"2020","journal-title":"Applied Soft Computing"},{"key":"key2023111509330289100_ref016","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1145\/191843.191925","article-title":"Fast subsequence matching in time-series databases","volume":"23","year":"1994","journal-title":"ACM Sigmod Record"},{"key":"key2023111509330289100_ref017","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ins.2015.07.046","article-title":"Time series clustering via community detection in networks","volume":"326","year":"2016","journal-title":"Information Sciences"},{"first-page":"965","article-title":"Matrix profile xii: mpdist: a novel time series distance measure to allow data mining in more challenging scenarios","year":"2018","key":"key2023111509330289100_ref018"},{"key":"key2023111509330289100_ref019","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1080\/03610918.2017.1280829","article-title":"Classification of time series using combination of DTW and LCSS dissimilarity measures","volume":"47","year":"2018","journal-title":"Communications in Statistics \u2013 Simulation and Computation"},{"issue":"11","key":"key2023111509330289100_ref020","first-page":"5409","article-title":"Time-series clustering based on the characterization of segment typologies","volume":"51","year":"2020","journal-title":"IEEE Transactions on Cybernetics"},{"key":"key2023111509330289100_ref021","unstructured":"Hong, D., Gu, Q. and Whitehouse, K. (2017), \u201cHigh-dimensional time series clustering via cross-predictability\u201d, in Singh, A. and Zhu, J. (Eds), Artificial Intelligence and Statistics, PMLR, FL, USA, pp. 642-651."},{"key":"key2023111509330289100_ref022","first-page":"1460","article-title":"Locally weighted ensemble clustering","volume":"48","year":"2017","journal-title":"IEEE Transactions on Cybernetics"},{"first-page":"1616","article-title":"Large-scale unusual time series detection","year":"2015","key":"key2023111509330289100_ref023"},{"key":"key2023111509330289100_ref024","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.patrec.2018.11.013","article-title":"A novel distance measure for time series: maximum shifting correlation distance","volume":"117","year":"2019","journal-title":"Pattern Recognition Letters"},{"key":"key2023111509330289100_ref025","doi-asserted-by":"crossref","first-page":"106701","DOI":"10.1016\/j.asoc.2020.106701","article-title":"Clustering time-series by a novel slope-based similarity measure considering particle swarm optimization","volume":"96","year":"2020","journal-title":"Applied Soft Computing"},{"key":"key2023111509330289100_ref026","doi-asserted-by":"crossref","unstructured":"Keogh, E., Chu, S., Hart, D. and Pazzani, M. (2004), \u201cSegmenting time series: a survey and novel approach\u201d, in Bunke, H. and Wang, P. S. P. (Eds), Data Mining in Time Series Databases, World Scientific, Singapore, pp. 1-21.","DOI":"10.1142\/9789812565402_0001"},{"key":"key2023111509330289100_ref027","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s10115-004-0154-9","article-title":"Exact indexing of dynamic time warping","volume":"7","year":"2005","journal-title":"Knowledge and Information Systems"},{"key":"key2023111509330289100_ref028","first-page":"239","article-title":"An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback","volume":"98","year":"1998","journal-title":"KDD"},{"key":"key2023111509330289100_ref029","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1016\/0031-3203(95)00052-6","article-title":"Syntactic recognition of ECG signals by attributed finite automata","volume":"28","year":"1995","journal-title":"Pattern Recognition"},{"key":"key2023111509330289100_ref030","doi-asserted-by":"crossref","first-page":"106919","DOI":"10.1016\/j.asoc.2020.106919","article-title":"Clustering-based anomaly detection in multivariate time series data","volume":"100","year":"2021","journal-title":"Applied Soft Computing"},{"first-page":"490","article-title":"Fast similarity search in the presence of noise, scaling, and translation in time-series databases","year":"1995","key":"key2023111509330289100_ref031"},{"key":"key2023111509330289100_ref032","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/asmb.2644","article-title":"Clustering high\u2010frequency financial time series based on information theory","volume":"38","year":"2022","journal-title":"Applied Stochastic Models in Business and Industry"},{"first-page":"911","article-title":"Understanding of internal clustering validation measures","year":"2010","key":"key2023111509330289100_ref033"},{"key":"key2023111509330289100_ref034","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.eswa.2016.06.012","article-title":"Hierarchical clustering of time series data with parametric derivative dynamic time warping","volume":"62","year":"2016","journal-title":"Expert Systems with Applications"},{"first-page":"25","article-title":"Distance and density clustering for time series data","year":"2017","key":"key2023111509330289100_ref035"},{"key":"key2023111509330289100_ref036","first-page":"1","article-title":"Robust clustering of COVID-19 cases across US counties using mixtures of asymmetric time series models with time varying and freely indexed covariates","volume":"49","year":"2022","journal-title":"Journal of Applied Statistics"},{"first-page":"653","article-title":"Two-stage time-series clustering approach under reducing time cost requirement","year":"2020","key":"key2023111509330289100_ref037"},{"first-page":"65","article-title":"A comparative study on weighting-based clustering techniques: time series data","year":"2018","key":"key2023111509330289100_ref038"},{"first-page":"127","article-title":"Longest common subsequences","year":"1994","key":"key2023111509330289100_ref039"},{"key":"key2023111509330289100_ref040","first-page":"1192","article-title":"Longest common subsequence based algorithm for measuring similarity between time series: a new approach","volume":"24","year":"2013","journal-title":"World Applied Sciences Journal"},{"key":"key2023111509330289100_ref041","doi-asserted-by":"crossref","unstructured":"Ralanamahatana, C.A., Lin, J., Gunopulos, D., Keogh, E., Vlachos, M. and Das, G. (2005), \u201cMining time series data\u201d, in Maimon, O. and Rokach, L. (Eds), Data Mining and Knowledge Discovery Handbook, Springer, New York, USA, pp. 1069-1103.","DOI":"10.1007\/0-387-25465-X_51"},{"key":"key2023111509330289100_ref042","doi-asserted-by":"crossref","unstructured":"Rokach, L. and Maimon, O. (2005), \u201cClustering methods\u201d, in Maimon, O. and Rokach, L. (Eds), Data Mining and Knowledge Discovery Handbook, Springer, New York, USA, pp. 321-352.","DOI":"10.1007\/0-387-25465-X_15"},{"key":"key2023111509330289100_ref043","doi-asserted-by":"crossref","first-page":"103664","DOI":"10.1016\/j.engappai.2020.103664","article-title":"DLCSS: a new similarity measure for time series data mining","volume":"92","year":"2020","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"key2023111509330289100_ref044","first-page":"1","article-title":"An autoencoder-based deep learning approach for clustering time series data","volume":"2","year":"2020","journal-title":"SN Applied Sciences"},{"first-page":"673","article-title":"Discovering similar multidimensional trajectories","year":"2002","key":"key2023111509330289100_ref045"},{"key":"key2023111509330289100_ref046","doi-asserted-by":"crossref","first-page":"6331","DOI":"10.1007\/s00500-018-3287-6","article-title":"Hierarchical clustering of unequal-length time series with area-based shape distance","volume":"23","year":"2019","journal-title":"Soft Computing"},{"first-page":"177","article-title":"Patterns of temporal variation in online media","year":"2011","key":"key2023111509330289100_ref047"},{"key":"key2023111509330289100_ref048","doi-asserted-by":"crossref","first-page":"11891","DOI":"10.1016\/j.eswa.2011.03.081","article-title":"A novel clustering method on time series data","volume":"38","year":"2011","journal-title":"Expert Systems with Applications"},{"key":"key2023111509330289100_ref049","article-title":"A review of subsequence time series clustering","volume":"2014","year":"2014","journal-title":"The Scientific World Journal"},{"key":"key2023111509330289100_ref050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2018.10.005","article-title":"Complex network approaches to nonlinear time series analysis","volume":"787","year":"2019","journal-title":"Physics Reports"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-08-2022-0300\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-08-2022-0300\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:15:19Z","timestamp":1753398919000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/57\/5\/756-779\/27243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,16]]},"references-count":50,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3,16]]},"published-print":{"date-parts":[[2023,11,15]]}},"alternative-id":["10.1108\/DTA-08-2022-0300"],"URL":"https:\/\/doi.org\/10.1108\/dta-08-2022-0300","relation":{},"ISSN":["2514-9288","2514-9288"],"issn-type":[{"type":"print","value":"2514-9288"},{"type":"electronic","value":"2514-9288"}],"subject":[],"published":{"date-parts":[[2023,3,16]]}}}