{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T17:33:49Z","timestamp":1758044029146,"version":"3.44.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100013360","name":"Invest Northern Ireland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013360","id-type":"DOI","asserted-by":"publisher"}]},{"name":"British Telecom"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Process task duration data often exhibit multiple peaks, indicating differences in, for example, customer ages and preferences, resource capabilities or the day\/hour of a week. This heterogeneous data, which captures diverse customer patterns, should be represented using different models, resulting in an overall mixture model. This paper introduces gamma mixture models to represent various customer patterns in task duration data, with a focus on automating the fitting process. The approach involves a two-stage procedure: first, divide-and-conquer using peak-, equidistance- and cluster-based techniques to partition data, and automatically fit gamma distributions to each subset. The second stage then improves the fitted mixture model by directly searching the log-likelihood surface. The method is compared with the expectation\u2013maximization (EM) algorithm and an open tool (HyperStar), using both artificially generated datasets and a publicly available hospital billing dataset, demonstrating its effectiveness and time efficiency in modelling heterogeneous process duration data. Furthermore, a case study on process conformance checking is conducted using the hospital billing dataset, highlighting a potential application area for the method in process mining.<\/jats:p>","DOI":"10.1007\/s10618-025-01131-5","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T13:45:05Z","timestamp":1753105505000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automating mixture model fitting of task durations for process conformance checking"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4991-6813","authenticated-orcid":false,"given":"Lingkai","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sally","family":"McClean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malcolm","family":"Faddy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Donnelly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kashaf","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Burke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"issue":"2","key":"1131_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2229156.2229157","volume":"3","author":"W van der Aalst","year":"2012","unstructured":"van der Aalst W (2012) Process mining: overview and opportunities. ACM Trans Manag Inf Syst (TMIS) 3(2):1\u201317","journal-title":"ACM Trans Manag Inf Syst (TMIS)"},{"issue":"4","key":"1131_CR2","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1109\/TSC.2012.25","volume":"6","author":"W van der Aalst","year":"2012","unstructured":"van der Aalst W (2012) Service mining: using process mining to discover, check, and improve service behavior. IEEE Trans Serv Comput 6(4):525\u2013535","journal-title":"IEEE Trans Serv Comput"},{"key":"1131_CR3","unstructured":"van\u00a0der Aalst W, Adriansyah A, De\u00a0Medeiros AKA, et\u00a0al (2011) Process mining manifesto. In: International conference on business process management, Springer, pp 169\u2013194"},{"key":"1131_CR4","doi-asserted-by":"crossref","unstructured":"van\u00a0der Aalst W, Brockhoff T, Ghahfarokhi AF, et\u00a0al (2020a) Removing operational friction using process mining: challenges provided by the internet of production (IOP). In: International conference on data management technologies and applications, Springer, pp 1\u201331","DOI":"10.1007\/978-3-030-83014-4_1"},{"key":"1131_CR5","doi-asserted-by":"crossref","unstructured":"van\u00a0der Aalst W, Tacke Genannt\u00a0Unterberg D, Denisov V, et\u00a0al (2020b) Visualizing token flows using interactive performance spectra. In: International conference on applications and theory of Petri Nets and Concurrency, Springer, pp 369\u2013380","DOI":"10.1007\/978-3-030-51831-8_18"},{"key":"1131_CR6","doi-asserted-by":"crossref","unstructured":"van\u00a0der Aalst WM (2022) Process mining: a 360 degree overview. In: Process mining handbook. Springer, pp 3\u201334","DOI":"10.1007\/978-3-031-08848-3_1"},{"key":"1131_CR7","doi-asserted-by":"crossref","unstructured":"van\u00a0der Aalst WM, Brockhoff T, Ghahfarokhi AF, et\u00a0al (2021) Removing operational friction using process mining: challenges provided by the internet of production (IOP). In: Data management technologies and applications: 9th international conference, DATA 2020, Virtual Event, July 7\u20139, 2020, Revised Selected Papers 9, Springer, pp 1\u201331","DOI":"10.1007\/978-3-030-83014-4_1"},{"issue":"2","key":"1131_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102844","volume":"59","author":"P An","year":"2022","unstructured":"An P, Wang Z, Zhang C (2022) Ensemble unsupervised autoencoders and gaussian mixture model for cyberattack detection. Inf Process Manag 59(2):102844","journal-title":"Inf Process Manag"},{"key":"1131_CR9","doi-asserted-by":"crossref","unstructured":"Antunes BB, Manresa A, Bastos LS, et\u00a0al (2019) A solution framework based on process mining, optimization, and discrete-event simulation to improve queue performance in an emergency department. In: International conference on business process management, Springer, pp 583\u2013594","DOI":"10.1007\/978-3-030-37453-2_47"},{"issue":"4","key":"1131_CR10","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1007\/s00180-012-0367-4","volume":"28","author":"L Bagnato","year":"2013","unstructured":"Bagnato L, Punzo A (2013) Finite mixtures of unimodal beta and gamma densities and the-bumps algorithm. Comput Stat 28(4):1571\u20131597","journal-title":"Comput Stat"},{"issue":"4","key":"1131_CR11","first-page":"117","volume":"17","author":"G Battineni","year":"2019","unstructured":"Battineni G (2019) Process mining case study approach: Extraction of unconventional event logs to improve performance in hospital information systems (his). Int J Comput Sci Inf Security (IJCSIS) 17(4):117\u2013128","journal-title":"Int J Comput Sci Inf Security (IJCSIS)"},{"key":"1131_CR12","first-page":"328","volume":"448","author":"L Blevi","year":"2017","unstructured":"Blevi L, Delporte L, Robbrecht J (2017) Process mining on the loan application process of a Dutch financial institute. BPI Chall 448:328\u2013343","journal-title":"BPI Chall"},{"key":"1131_CR13","doi-asserted-by":"crossref","unstructured":"Boersma HJ, Leung TI, Vanwersch R, et\u00a0al (2019) Optimizing care processes with operational excellence & process mining. Fundamentals of clinical data science pp 181\u2013192","DOI":"10.1007\/978-3-319-99713-1_13"},{"key":"1131_CR14","doi-asserted-by":"publisher","first-page":"13727","DOI":"10.1109\/ACCESS.2021.3051758","volume":"9","author":"S Bourouis","year":"2021","unstructured":"Bourouis S, Sallay H, Bouguila N (2021) A competitive generalized gamma mixture model for medical image diagnosis. IEEE Access 9:13727\u201313736","journal-title":"IEEE Access"},{"key":"1131_CR15","doi-asserted-by":"crossref","unstructured":"Brockhoff T, Uysal MS, van\u00a0der Aalst W (2020a) Time-aware concept drift detection using the earth mover\u2019s distance. In: 2020 2nd international conference on process mining (ICPM), IEEE, pp 33\u201340","DOI":"10.1109\/ICPM49681.2020.00016"},{"key":"1131_CR16","doi-asserted-by":"crossref","unstructured":"Brockhoff T, Uysal MS, van\u00a0der Aalst WM (2020b) Time-aware concept drift detection using the earth mover\u2019s distance. In: 2020 2nd international conference on process mining (ICPM), IEEE, pp 33\u201340","DOI":"10.1109\/ICPM49681.2020.00016"},{"key":"1131_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06674-5","volume-title":"Input modeling with phase-type distributions and Markov models: theory and applications","author":"P Buchholz","year":"2014","unstructured":"Buchholz P, Kriege J, Felko I (2014) Input modeling with phase-type distributions and Markov models: theory and applications. Springer, Berlin"},{"issue":"2","key":"1131_CR18","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1177\/0049124104268644","volume":"33","author":"KP Burnham","year":"2004","unstructured":"Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33(2):261\u2013304","journal-title":"Sociol Methods Res"},{"key":"1131_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2023.102248","volume":"117","author":"M Camargo","year":"2023","unstructured":"Camargo M, B\u00e1ron D, Dumas M et al (2023) Learning business process simulation models: a hybrid process mining and deep learning approach. Inf Syst 117:102248","journal-title":"Inf Syst"},{"key":"1131_CR20","doi-asserted-by":"crossref","unstructured":"Carmona J, van Dongen B, Weidlich M (2022) Conformance checking: foundations, milestones and challenges. In: Process mining handbook. Springer, pp 155\u2013190","DOI":"10.1007\/978-3-031-08848-3_5"},{"issue":"12","key":"1131_CR21","doi-asserted-by":"publisher","first-page":"9654","DOI":"10.1109\/TPAMI.2021.3128271","volume":"44","author":"W Fan","year":"2021","unstructured":"Fan W, Yang L, Bouguila N (2021) Unsupervised grouped axial data modeling via hierarchical Bayesian nonparametric models with Watson distributions. IEEE Trans Pattern Anal Mach Intell 44(12):9654\u20139668","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1131_CR22","doi-asserted-by":"crossref","unstructured":"Fracca C, de\u00a0Leoni M, Asnicar F, et\u00a0al (2022) Estimating activity start timestamps in the presence of waiting times via process simulation. In: International conference on advanced information systems engineering, Springer, pp 287\u2013303","DOI":"10.1007\/978-3-031-07472-1_17"},{"key":"1131_CR23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s12599-020-00649-w","volume":"63","author":"T Graafmans","year":"2021","unstructured":"Graafmans T, Turetken O, Poppelaars H et al (2021) Process mining for six sigma: a guideline and tool support. Bus Inf Syst Eng 63:277\u2013300","journal-title":"Bus Inf Syst Eng"},{"issue":"1","key":"1131_CR24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3390\/buildings11010030","volume":"11","author":"M Han","year":"2021","unstructured":"Han M, Wang Z, Zhang X (2021) An approach to data acquisition for urban building energy modeling using a Gaussian mixture model and expectation-maximization algorithm. Buildings 11(1):30","journal-title":"Buildings"},{"issue":"2","key":"1131_CR25","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1002\/hpm.3593","volume":"38","author":"T Kropp","year":"2023","unstructured":"Kropp T, Faeghi S, Lennerts K (2023) Evaluation of patient transport service in hospitals using process mining methods: patients\u2019 perspective. Int J Health Plann Manag 38(2):430\u2013456","journal-title":"Int J Health Plann Manag"},{"issue":"7","key":"1131_CR26","doi-asserted-by":"publisher","first-page":"4277","DOI":"10.1002\/int.22721","volume":"37","author":"Y Lai","year":"2022","unstructured":"Lai Y, Guan W, Luo L et al (2022) Extended variational inference for Dirichlet process mixture of Beta-Liouville distributions for proportional data modeling. Int J Intell Syst 37(7):4277\u20134306","journal-title":"Int J Intell Syst"},{"key":"1131_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2024.102434","volume":"126","author":"K Lashkevich","year":"2024","unstructured":"Lashkevich K, Milani F, Chapela-Campa D et al (2024) Unveiling the causes of waiting time in business processes from event logs. Inf Syst 126:102434","journal-title":"Inf Syst"},{"key":"1131_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2021.101724","volume":"102","author":"SJ Leemans","year":"2021","unstructured":"Leemans SJ, van der Aalst WM, Brockhoff T et al (2021) Stochastic process mining: earth movers\u2019 stochastic conformance. Inf Syst 102:101724","journal-title":"Inf Syst"},{"key":"1131_CR29","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.patcog.2018.10.025","volume":"87","author":"C Liu","year":"2019","unstructured":"Liu C, Li HC, Fu K et al (2019) Bayesian estimation of generalized gamma mixture model based on variational EM algorithm. Pattern Recogn 87:269\u2013284","journal-title":"Pattern Recogn"},{"key":"1131_CR30","doi-asserted-by":"crossref","unstructured":"Mannhardt F, de\u00a0Leoni M, Reijers HA, et\u00a0al (2017) Data-driven process discovery-revealing conditional infrequent behavior from event logs. In: International conference on advanced information systems engineering, Springer, pp 545\u2013560","DOI":"10.1007\/978-3-319-59536-8_34"},{"issue":"24","key":"1131_CR31","doi-asserted-by":"publisher","first-page":"5001","DOI":"10.3390\/math11245001","volume":"11","author":"S McClean","year":"2023","unstructured":"McClean S, Yang L (2023) Semi-Markov models for process mining in smart homes. Mathematics 11(24):5001","journal-title":"Mathematics"},{"key":"1131_CR32","volume-title":"The EM algorithm and extensions","author":"GJ McLachlan","year":"2007","unstructured":"McLachlan GJ, Krishnan T (2007) The EM algorithm and extensions. Wiley, Hoboken"},{"key":"1131_CR33","doi-asserted-by":"crossref","unstructured":"Okamura H, Dohi T (2016) Fitting phase-type distributions and markovian arrival processes: Algorithms and tools. In: Principles of performance and reliability modeling and evaluation. Springer, pp 49\u201375","DOI":"10.1007\/978-3-319-30599-8_3"},{"issue":"4","key":"1131_CR34","first-page":"401","volume":"7","author":"H Okamura","year":"2013","unstructured":"Okamura H, HIRATA T, Tadashi D (2013) Semi-parametric approach for software reliability evaluation using mixed gamma distributions. Int J Software Eng Appl 7(4):401\u2013414","journal-title":"Int J Software Eng Appl"},{"issue":"1","key":"1131_CR35","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1146\/annurev-statistics-030718-104938","volume":"6","author":"VM Panaretos","year":"2019","unstructured":"Panaretos VM, Zemel Y (2019) Statistical aspects of Wasserstein distances. Annual Rev Stat Appl 6(1):405\u2013431","journal-title":"Annual Rev Stat Appl"},{"key":"1131_CR36","doi-asserted-by":"crossref","unstructured":"Quintano\u00a0Neira RA, Hompes BFA, Vries J, et\u00a0al (2019) Analysis and optimization of a sepsis clinical pathway using process mining. In: International conference on business process management, Springer, pp 459\u2013470","DOI":"10.1007\/978-3-030-37453-2_37"},{"key":"1131_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2021.101908","volume":"134","author":"M Rafiei","year":"2021","unstructured":"Rafiei M, van der Aalst W (2021) Group-based privacy preservation techniques for process mining. Data Know Eng 134:101908","journal-title":"Data Know Eng"},{"issue":"4","key":"1131_CR38","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1504\/IJBPIM.2017.088819","volume":"8","author":"H R\u2019bigui","year":"2017","unstructured":"R\u2019bigui H, Cho C (2017) The state-of-the-art of business process mining challenges. Int J Bus Process Integr Manag 8(4):285\u2013303","journal-title":"Int J Bus Process Integr Manag"},{"issue":"12","key":"1131_CR39","doi-asserted-by":"publisher","first-page":"3840","DOI":"10.1016\/j.camwa.2012.03.016","volume":"64","author":"P Reinecke","year":"2012","unstructured":"Reinecke P, Krau\u00df T, Wolter K (2012) Cluster-based fitting of phase-type distributions to empirical data. Comput Math Appl 64(12):3840\u20133851","journal-title":"Comput Math Appl"},{"key":"1131_CR40","doi-asserted-by":"crossref","unstructured":"Reinecke P, Krau\u00df T, Wolter K (2013) Phase-type fitting using hyperstar. In: European workshop on performance engineering, Springer, pp 164\u2013175","DOI":"10.1007\/978-3-642-40725-3_13"},{"key":"1131_CR41","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.is.2018.11.003","volume":"84","author":"F Richter","year":"2019","unstructured":"Richter F, Seidl T (2019) Looking into the tesseract: time-drifts in event streams using series of evolving rolling averages of completion times. Inf Syst 84:265\u2013282","journal-title":"Inf Syst"},{"key":"1131_CR42","doi-asserted-by":"crossref","unstructured":"Riska A, Diev V, Smirni E (2002) Efficient fitting of long-tailed data sets into hyperexponential distributions. In: Global telecommunications conference, 2002. GLOBECOM\u201902. IEEE, IEEE, pp 2513\u20132517","DOI":"10.1109\/GLOCOM.2002.1189083"},{"issue":"1\u20132","key":"1131_CR43","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/S0166-5316(03)00101-9","volume":"55","author":"A Riska","year":"2004","unstructured":"Riska A, Diev V, Smirni E (2004) An EM-based technique for approximating long-tailed data sets with PH distributions. Perform Eval 55(1\u20132):147\u2013164","journal-title":"Perform Eval"},{"key":"1131_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2015.04.004","volume":"54","author":"A Rogge-Solti","year":"2015","unstructured":"Rogge-Solti A, Weske M (2015) Prediction of business process durations using non-Markovian stochastic petri nets. Inf Syst 54:1\u201314","journal-title":"Inf Syst"},{"key":"1131_CR45","doi-asserted-by":"crossref","unstructured":"Rojas E, Cifuentes A, Burattin A, et\u00a0al (2018) Analysis of emergency room episodes duration through process mining. In: International conference on business process management, Springer, pp 251\u2013263","DOI":"10.1007\/978-3-030-11641-5_20"},{"issue":"6","key":"1131_CR46","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1007\/s10514-017-9686-1","volume":"42","author":"JJ Rold\u00e1n","year":"2018","unstructured":"Rold\u00e1n JJ, Olivares-M\u00e9ndez MA, del Cerro J et al (2018) Analyzing and improving multi-robot missions by using process mining. Auton Robot 42(6):1187\u20131205","journal-title":"Auton Robot"},{"issue":"1","key":"1131_CR47","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/computers10010006","volume":"10","author":"H Sallay","year":"2020","unstructured":"Sallay H, Bourouis S, Bouguila N (2020) Online learning of finite and infinite gamma mixture models for covid-19 detection in medical images. Computers 10(1):6","journal-title":"Computers"},{"issue":"2","key":"1131_CR48","first-page":"117","volume":"2","author":"M Selvam","year":"2022","unstructured":"Selvam M, Ramachandran M, Saravanan V (2022) Nelder-Mead simplex search method-a study. Data Anal Artif Intell 2(2):117\u2013122","journal-title":"Data Anal Artif Intell"},{"issue":"2","key":"1131_CR49","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.ejor.2023.09.010","volume":"317","author":"A Stevens","year":"2023","unstructured":"Stevens A, De Smedt J (2023) Explainability in process outcome prediction: guidelines to obtain interpretable and faithful models. Eur J Oper Res 317(2):317\u2013329","journal-title":"Eur J Oper Res"},{"issue":"9","key":"1131_CR50","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1109\/TPAMI.2014.2306426","volume":"36","author":"J Taghia","year":"2014","unstructured":"Taghia J, Ma Z, Leijon A (2014) Bayesian estimation of the von-mises fisher mixture model with variational inference. IEEE Trans Pattern Anal Mach Intell 36(9):1701\u20131715","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"1131_CR51","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/s11227-008-0173-5","volume":"45","author":"J Wang","year":"2008","unstructured":"Wang J, Liu J, She C (2008) Segment-based adaptive hyper-erlang model for long-tailed network traffic approximation. J Supercomput 45(3):296\u2013312","journal-title":"J Supercomput"},{"issue":"3","key":"1131_CR52","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1198\/106186001317115054","volume":"10","author":"M Wiper","year":"2001","unstructured":"Wiper M, Insua DR, Ruggeri F (2001) Mixtures of gamma distributions with applications. J Comput Graph Stat 10(3):440\u2013454","journal-title":"J Comput Graph Stat"},{"key":"1131_CR53","doi-asserted-by":"crossref","unstructured":"Yang L, McClean S, Donnelly M et al (2021) Process duration modelling and concept drift detection for business process mining. In: 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing. Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/IOP\/SCI), IEEE, pp 653\u2013658","DOI":"10.1109\/SWC50871.2021.00097"},{"key":"1131_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118533","volume":"210","author":"L Yang","year":"2022","unstructured":"Yang L, McClean S, Donnelly M et al (2022) A multi-components approach to monitoring process structure and customer behaviour concept drift. Expert Syst Appl 210:118533","journal-title":"Expert Syst Appl"},{"issue":"8","key":"1131_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3669942","volume":"18","author":"L Yang","year":"2024","unstructured":"Yang L, McClean S, Donnelly M et al (2024) Detecting process duration drift using gamma mixture models in a left-truncated and right-censored environment. ACM Trans Knowl Discov Data 18(8):1\u201324","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1131_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125365","volume":"260","author":"L Yang","year":"2025","unstructured":"Yang L, Cheng J, Luo Y et al (2025) Detecting and rationalizing concept drift: a feature-level approach for understanding cause\u2014effect relationships in dynamic environments. Expert Syst Appl 260:125365","journal-title":"Expert Syst Appl"},{"key":"1131_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2025.102430","volume":"158","author":"L Yang","year":"2025","unstructured":"Yang L, McClean S, Burke K et al (2025) Modelling process durations with gamma mixtures for right-censored data: applications in customer clustering, pattern recognition, drift detection, and rationalisation. Data Knowl Eng 158:102430","journal-title":"Data Knowl Eng"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01131-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-025-01131-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01131-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T10:30:20Z","timestamp":1757673020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-025-01131-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,21]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["1131"],"URL":"https:\/\/doi.org\/10.1007\/s10618-025-01131-5","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2025,7,21]]},"assertion":[{"value":"21 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2025","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"53"}}