Gotowe bibliografie tematyczne / Trace Clustering

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Trace Clustering”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Trace Clustering"

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LI, Huiling, Shuaipeng ZHANG, and Xuan SU. "Trace Clustering: A Preprocessing Method to Improve the Performance of Process Discovery." International Journal of Science and Engineering Applications 10, no. 09 (2021): 116–21. http://dx.doi.org/10.7753/ijsea1009.1001.

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The information system collects a large number of business process event logs, and process discovery aims to discover process models from the event logs. Many process discovery methods have been proposed, but most of them still have problems when processing event logs, such as low mining efficiency and poor process model quality. The trace clustering method allows to decompose original log to effectively solve these problems. There are many existing trace clustering methods, such as clustering based on vector space approaches, context-aware trace clustering, model-based sequence clustering, et
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Hachicha, Wiem, Leila Ghorbel, Ronan Champagnat, Corinne Amel Zayani, Mourad Rabah, and Samuel Nowakowski. "Proposition d’une architecture utilisant le trace clustering pour recommander un parcours d’apprentissage : définition des couches de fouille de processus et de recommandation." Revue ouverte d’ingénierie des systèmes d’information 5, no. 1 (2025): 70–94. https://doi.org/10.21494/iste.op.2025.1300.

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Les systèmes d’informations pédagogiques permettent d’observer les traces d’apprentissage des apprenants et de mener des analyses sur leurs pratiques ou de prédire leur réussite. Dans ces travaux, nous étudions comment la fouille de processus, qui permet d’extraire des modèles de comportement des utilisateurs dans un système d’information, peut être utilisée dans un système de recommandation contextuel. Nous nous concentrons plus particulièrement sur le trace clustering qui vise à regrouper des traces possédant des dynamiques proches. Nos apports portent sur : la définition d’une architecture
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De Weerdt, Jochen, Seppe vanden Broucke, Jan Vanthienen, and Bart Baesens. "Active Trace Clustering for Improved Process Discovery." IEEE Transactions on Knowledge and Data Engineering 25, no. 12 (2013): 2708–20. http://dx.doi.org/10.1109/tkde.2013.64.

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Delias, Pavlos, Michael Doumpos, Evangelos Grigoroudis, and Nikolaos Matsatsinis. "A non‐compensatory approach for trace clustering." International Transactions in Operational Research 26, no. 5 (2017): 1828–46. http://dx.doi.org/10.1111/itor.12395.

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Grigore, Iuliana Malina, Gabriel Marques Tavares, Matheus Camilo da Silva, Paolo Ceravolo, and Sylvio Barbon Junior. "Automated Trace Clustering Pipeline Synthesis in Process Mining." Information 15, no. 4 (2024): 241. http://dx.doi.org/10.3390/info15040241.

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Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace
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Dobrota, Milan, Boris Delibašić, and Pavlos Delias. "A Skiing Trace Clustering Model for Injury Risk Assessment." International Journal of Decision Support System Technology 8, no. 1 (2016): 56–68. http://dx.doi.org/10.4018/ijdsst.2016010104.

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This paper investigates the relation between skiing movement activity patterns and risk of injury. The goal is to provide a framework which can be used for estimating the level of skiers' injury risks, based on skiing patterns. Data, collected from ski-lift gates in the form of process event logs is analyzed. After initial transformation of data into traces, trace vectors, and similarity matrix, using several clustering methods different skiing patterns are identified and compared. The quality of clusters is determined by how well clusters discriminate between injured and noninjured skiers. Th
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Imran, Mohammad, Maizatul Akmar Ismail, Suraya Hamid, and Mohammad Hairul Nizam Md Nasir. "A TRACE CLUSTERING FRAMEWORK FOR IMPROVING THE BEHAVIORAL AND STRUCTURAL QUALITY OF PROCESS MODELS IN PROCESS MINING." Malaysian Journal of Computer Science 36, no. 3 (2023): 223–41. http://dx.doi.org/10.22452/mjcs.vol36no3.2.

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Process mining (PM) techniques are increasingly used to enhance operational procedures. However, applying PM to unstructured processes can result in complex process models that are difficult to interpret. Trace clustering is the most prevalent method for handling this complexity, but it has limitations in dealing with event logs that contain many activities with varied behaviours. In such cases, trace clustering can produce inaccurate process models that are expensive in terms of time performance. Therefore, it is crucial to develop a trace clustering solution that is optimal in terms of behav
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De Koninck, Pieter, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, and Jochen De Weerdt. "Expert-driven trace clustering with instance-level constraints." Knowledge and Information Systems 63, no. 5 (2021): 1197–220. http://dx.doi.org/10.1007/s10115-021-01548-6.

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Faizan, Muhammad, Megat F. Zuhairi, and Shahrinaz Ismail. "Process Discovery Enhancement with Trace Clustering and Profiling." Annals of Emerging Technologies in Computing 5, no. 4 (2021): 1–13. http://dx.doi.org/10.33166/aetic.2021.04.001.

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The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured pr
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Nickolayev, Oleg Y., Philip C. Roth, and Daniel A. Reed. "Real-Time Statistical Clustering for Event Trace Reduction." International Journal of Supercomputer Applications and High Performance Computing 11, no. 2 (1997): 144–59. http://dx.doi.org/10.1177/109434209701100207.

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Rozprawy doktorskie na temat "Trace Clustering"

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Abonyi, J., FD Tamás, S. Potgieter, and H. Potgieter. "Analysis of Trace Elements in South African Clinkers using Latent Variable Model and Clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000893.

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The trace element content of clinkers (and possibly of cements) can be used to identify the manufacturing factory. The Mg, Sr, Ba, Mn, Ti, Zr, Zn and V content of clinkers give detailed information for the determination of the origin of clinkers produced in different factories. However, for the analysis of such complex data there is a need for algorithmic tools for the visualization and clustering of the samples. This paper proposes a new approach for this purpose. The analytical data are transformed into a twodimensional latent space by factor analysis (probabilistic principal component analy
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Abonyia, J., FD Tamas, and S. Potgieter. "Analysis of trace elements in South African clinkers using latent variable model and clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001952.

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Abstract The trace element content of clinkers (and possibly of cements) can be used to identify the manufacturing factory. The Mg, Sr, Ba, Mn, Ti, Zr, Zn and V content of clinkers give detailed information for the determination of the origin of clinkers produced in different factories. However, for the analysis of such complex data there is a need for algorithmic tools for the visualization and clustering of the samples. This paper proposes a new approach for this purpose. The analytical data are transformed into a twodimensional latent space by factor analysis (probabilistic principal compon
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Iegorov, Oleg. "Une approche de fouille de données pour le débogage temporel des applications embarquées de streaming." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM032/document.

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Le déboggage des applications de streaming qui s'exécutent sur les systèmes embarqués multimédia est l'un des domaines les plus exigeants dans le développement de logiciel embarqué. Les nouvelles générations de materiel embarqué introduisent de nouvelles systèmes sur une puce, qui fait que les développeurs du logiciel doivent adapter leurs logiciels aux nouvelles platformes. Le logiciel embarqué doit non seulement fournir des résultats corrects mais aussi le faire en temps réel afin de respecter les propriétés de qualité de service (Quality-of-Service, QoS) du système. Lorsque les propriétés Q
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Dos, Santos Morgane. "Modélisation de la topologie des dépôts d’énergie créés par un rayonnement ionisant à l’échelle nanométrique dans les noyaux cellulaires et relation avec les événements précoces radio-­induits." Thesis, Bordeaux 1, 2013. http://www.theses.fr/2013BOR14865/document.

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Les rayonnements ionisants sont connus pour induire des dommages critiques au sein de la matière biologique et spécialement au sein de l’ADN. Parmi ces dommages, les cassures doubles brins de l’ADN (DSB) sont considérées comme les principales responsables des effets létaux des rayonnements. Comprendre et prédire comment ces cassures sont créées et réparées dans les noyaux cellulaires demeure un défi dans la recherche en radiobiologie. Ce travail s’inscrit dans ce contexte, dans la modélisation des cassures double brin de l’ADN (DSB) à partir des dépôts d’énergie créés par l’irradiation au nive
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Peltonen, Joanna. "Effective Spatial Mapping for Coupled Code Analysis of Thermal–Hydraulics/Neutron–Kinetics of Boiling Water Reactors." Doctoral thesis, KTH, Kärnkraftsäkerhet, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122088.

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Analyses of nuclear reactor safety have increasingly required coupling of full three dimensional neutron kinetics (NK) core models with system transient thermal–hydraulics (TH) codes.  In order to produce results within a reasonable computing time, the coupled codes use two different spatial description of the reactor core.  The TH code uses few, typically 5 to 20 TH channels, which represent the core.  The NK code uses explicit one node for each fuel assembly.  Therefore, a spatial mapping of a coarse grid TH and a fine grid NK domain is necessary.  However, improper mappings may result in lo
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Petraro, Alessandro. "Clustering di tracce di mobilità per l’identificazione di stili di guida." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13003/.

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Traffic simulators are effective tools to support decisions in urban planning systems, to identify criticalities, to observe emerging behaviours in road networks and to configure road infrastructures, such as road side units and traffic lights. Clearly the more realistic the simulator the more precise the insight provided to decision makers. This paper provides a first step toward the design and calibration of traffic micro-simulator to produce realistic behaviour. The long term idea is to collect and analyse real traffic traces collecting vehicular information, to cluster them in groups repre
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Makuwaza, Gwynneth Chandakaita. "Clustering as a strategy for manufacturing performance in the Eastern Cape Automotive industry." Thesis, Rhodes University, 2001. http://hdl.handle.net/10962/d1002670.

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South Africa's current industrial policy focuses on the economy at two levels: a sectoral and spatial level. The former relates to the manner in which industrial policy is concentrating on particular sectors i.e. industrial clusters. This shift in industrial policy from targeting individual industries to an emphasis on industrial clusters, has complemented the change in focus to export promotion under trade policy. Furthermore, there has been increasing recognition of the need to improve industry performance and competitiveness in order to successfully implement export growth. Industry cluster
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Anzano, Eugeno S. "Fuzzy Clustering Means algorithm for track fusion in U.S. Coast Guard Vessel Traffic Service systems /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA368496.

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Anzano, Eugenio S. "Fuzzy Clustering Means algorithm for track fusion in U.S. Coast Guard Vessel Traffic Service systems." Thesis, Monterey, California: Naval Postgraduate School, 1999. http://hdl.handle.net/10945/13489.

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This thesis presents a fuzzy association based data fusion algorithm for U.S. Coast Guard Vessel Traffic Service (VTS) systems to reduce the number of redundant target tracks displayed to vessel traffic operators. The proposed algorithm uses the Fuzzy Clustering Means (FCM) algorithm to reduce the number of target tracks and associate duplicate tracks by determining the degree of membership for each target track. The algorithm uses current sensor data and the known sensor resolutions for measurement-to-measurement association and the selection of the most accurate sensor for tracking fused tar
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Qin, Tian. "Estimation of Water Demands Using an MCMC Algorithm with Clustering Methods." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002222852385.

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Książki na temat "Trace Clustering"

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Rice economy and Kerala model of clustering. Serials Publications, 2009.

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Fuzzy Clustering Means Algorithm for Track Fusion in U.S. Coast Guard Vessel Traffic Service Systems. Storming Media, 1999.

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Getahun, Tigabu Degu. Industrial Clustering, Firm Performance and Employee Welfare: Evidence from the Shoe and Flower Cluster in Ethiopia. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2016.

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Visser, Evert-Jan. Local Sources of Competitiveness: Spatial Clustering and Organisational Dynamics in Small-Scale Clothing in Lima, Peru (Tinbergen Institute Research Series no. 133). Purdue University Press, 1996.

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Getahun, Tigabu Degu. Industrial Clustering, Firm Performance and Employee Welfare: Evidence from the Shoe and Flower Cluster in Ethiopia. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2016.

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Getahun, Tigabu Degu. Industrial Clustering, Firm Performance and Employee Welfare: Evidence from the Shoe and Flower Cluster in Ethiopia. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2016.

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Industrial Clustering, Firm Performance and Employee Welfare: Evidence from the Shoe and Flower Cluster in Ethiopia. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2016.

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Christ, Julian Phillip. Innovative Places in Europe: Research Clustering, Co-Patenting Networks and the Growth of Regions. Lang Publishing, Incorporated, Peter, 2012.

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Benestad, Rasmus. Climate in the Barents Region. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.655.

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The Barents Sea is a region of the Arctic Ocean named after one of its first known explorers (1594–1597), Willem Barentsz from the Netherlands, although there are accounts of earlier explorations: the Norwegian seafarer Ottar rounded the northern tip of Europe and explored the Barents and White Seas between 870 and 890 ce, a journey followed by a number of Norsemen; Pomors hunted seals and walruses in the region; and Novgorodian merchants engaged in the fur trade. These seafarers were probably the first to accumulate knowledge about the nature of sea ice in the Barents region; however, scienti
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Części książek na temat "Trace Clustering"

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De Weerdt, Jochen. "Trace Clustering." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_91-1.

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Weerdt, Jochen De. "Trace Clustering." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_91.

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Chatain, Thomas, Josep Carmona, and Boudewijn van Dongen. "Alignment-Based Trace Clustering." In Conceptual Modeling. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69904-2_24.

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Song, Minseok, Christian W. Günther, and Wil M. P. van der Aalst. "Trace Clustering in Process Mining." In Business Process Management Workshops. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00328-8_11.

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Chen, Ding-Hua, Dong Huang, Haiyan Cheng, and Chang-Dong Wang. "D-TRACE: Deep Triply-Aligned Clustering." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_44.

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Wang, Pan, Wen’an Tan, Anqiong Tang, and Kai Hu. "A Novel Trace Clustering Technique Based on Constrained Trace Alignment." In Human Centered Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74521-3_7.

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Martinez-Carrasco, Antonio Lopez, Jose M. Juarez, Manuel Campos, Antonio Morales, Francisco Palacios, and Lucia Lopez-Rodriguez. "Interpretable Patient Subgrouping Using Trace-Based Clustering." In Artificial Intelligence in Medicine. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21642-9_33.

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Tavares, Gabriel Marques, Sylvio Barbon Junior, Ernesto Damiani, and Paolo Ceravolo. "Selecting Optimal Trace Clustering Pipelines with Meta-learning." In Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21686-2_11.

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De Koninck, Pieter, and Jochen De Weerdt. "Multi-objective Trace Clustering: Finding More Balanced Solutions." In Business Process Management Workshops. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58457-7_4.

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Accorsi, Rafael, and Thomas Stocker. "Discovering Workflow Changes with Time-Based Trace Clustering." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34044-4_9.

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Streszczenia konferencji na temat "Trace Clustering"

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Ma, Siqiang, Zhangyu Chen, Bo Wei, and Xu Yang. "Passive Location Discrete Point Trace Clustering Algorithm Based on Improved DBSCAN and ACO." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653330.

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Dong, Shuo, and Yinlin Wu. "Distributed multisensor track to track association algorithm based on improved clustering." In 2025 5th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2025), edited by Peicheng Zhu and Guihua Lin. SPIE, 2025. https://doi.org/10.1117/12.3070426.

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You, Bijia, and Zhiyun Xia. "A Hybrid Density-Based Clustering Pipeline for Track Reconstruction." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957781.

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Aliyah, Nurul Fitri, Sinung Suakanto, and Faqih Hamami. "Analysis Track Condition Trends Through Sensor Data and Clustering Techniques." In 2024 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2024. https://doi.org/10.1109/icitsi65188.2024.10929378.

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Pattnaik, Anup, Cijo George, Rishabh Kumar Tripathi, Sasanka Vutla, and Jithendra Vepa. "Improving Hierarchical Text Clustering with LLM-guided Multi-view Cluster Representation." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-industry.54.

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R. Neubaer, Thais, Marcelo Fantinato, and Sarajane M. Peres. "Interactive trace clustering." In XV Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2019. http://dx.doi.org/10.5753/sbsi.2019.7438.

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Process mining aims to automatically discover, analyze and improve business processes. Trace clustering is a task commonly used to reduce the inherent complexity of processes by identifying patterns. This research focuses on the application of experts knowledge in process mining through interactive clustering, referred to herein as interactive trace clustering. The aim is to improve trace clustering by reducing potential losses arising from arbitrary assumptions on the similarity between the datapoints, what is commonly required in unsupervised scenarios. Initial experiments considered partiti
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Luna, Mateus Alex dos Santos, André Paulino Lima, Thaís Rodrigues Neubauer, Marcelo Fantinato, and Sarajane Marques Peres. "Vector space models for trace clustering: a comparative study." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18274.

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Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algo
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N, Lakshmi Narayana, and Jagadishwari V. "Trace Clustering Techniques for Process Mining." In 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2023. http://dx.doi.org/10.1109/icaect57570.2023.10117842.

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De Sousa, Rafael Gaspar, and Sarajane Marques Peres. "Online concept drift detection, localization andcharacterization using trace clustering." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2020. http://dx.doi.org/10.5753/sbsi.2020.13122.

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Most process mining techniques assume stationary processes and are not well equipped to deal with concept drift. Online detection, localization and characterization of concept drift in business processes can support process mining techniques and analysts to improve organizations flexibility and adaptability. In this research, we propose a method to detect, locate and characterize concept drift in an online setting using trace clustering. The hypothesis is that the method can benefit from the trace clustering capacity to simplify complex problems through grouping similar patterns. In preliminar
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De Koninck, Pieter, and Jochen De Weerdt. "Scalable Mixed-Paradigm Trace Clustering using Super-Instances." In 2019 International Conference on Process Mining (ICPM). IEEE, 2019. http://dx.doi.org/10.1109/icpm.2019.00014.

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Raporty organizacyjne na temat "Trace Clustering"

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Pulido-Mahecha, Karen L., Sergio Restrepo-Ángel, and Franky Juliano Galeano-Ramírez†. Measuring the Unmeasurable: Unraveling the complexities of real-time output gap estimation. Banco de la República, 2024. http://dx.doi.org/10.32468/be.1284.

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This paper evaluates seven output gap models for real-time estimates, based on three criteria: stability of estimations on new observations, data revisions and/or methodological changes; inflation forecasting accuracy; and potential output response to structural economic shocks. Results confirm no single model outperforms across all criteria. Structural VARs exhibit superior inflation forecasts but show high instability, while semi-structural models produce more theoretically consistent potential output responses. To overcome this trade-off, we propose a novel clustering approach to pool model
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Navajas, Fernando. Electricity Rate Structure Design in Latin America: Where Do We Stand? Where Should We Go? Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005102.

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This paper reviews some critical issues for addressing the structure of electricity tariffs for ulterior purposes of policy research agenda. Starting from economic principles behind electricity tariff design, this paper asks what options ahead Latin America has in terms of improving electricity tariff design from a heterogeneous status quo, where trade-offs among cost recovery, cost reflectivity and affordability stand out. Options look like an avenue for improving cost recovery through better wholesale market design and regulation; move outside excess volumetric pricing and towards fixed and
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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, et al. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, 2022. http://dx.doi.org/10.36687/inetwp173.

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In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this indu
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Bonnett, Michaela, Angela Ladetto, Meaghan Kennedy, Jasmine Fernandez, and Teri Garstka. Network Analysis of a Mobility Ecosystem in Detroit, MI. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/hejw8941https://www.orangesparkleball.com/innovation-library-blog/2024/5/30/sunbelt2024-network-analysis-of-a-mobility-ecosystem-in-detroit-mi.

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Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition,
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Bonnett, Michaela, Angela Ladetto, Meaghan Kennedy, Jasmine Fernandez, and Teri Garstka. Network Analysis of a Mobility Ecosystem in Detroit, MI. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/hejw8941.

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Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition,
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Paynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, et al. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.

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Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence
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