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Journal articles on the topic 'Trace Clustering'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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Yu, Weizhong, Rong Wang, Feiping Nie, and Fei Wang. "Multi-view embedded clustering with unsupervised trace ratio LDA." Neurocomputing 315 (November 2018): 169–76. http://dx.doi.org/10.1016/j.neucom.2018.07.014.

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Melnyk, Roman, and Vitalii Vorobii. "PCB Image Defects Detection by Artificial Neural Networks and Resistance Analysis." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 23 (May 23, 2024): 70–83. http://dx.doi.org/10.37394/23201.2024.23.7.

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The approach contains the sequence of algorithms and formulas for image processing. They are single-layer neural networks, thinning, clustering, mathematical image comparison, and measurements of the trace length and width. All these procedures solve the task of selection and separation of the main objects in the printed circuit board: contacts, traces, and defects. The calculated features connect the conductance resistance of traces with the characteristics of defects. Imposing a tolerance on the distributed or concentrated changes of resistance it is possible to mark the defective and suspic

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Hendra, Darmawan, Sarno Riyanarto, Solichah Ahmadiyah Adhatus, Rossa Sungkono Kelly, and Sekar Wahyuni Cahyaningtyas. "Anomaly Detection based on Control-flow Pattern of Parallel Business Processes." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 6 (2018): 2809–16. https://doi.org/10.12928/TELKOMNIKA.v16i6.10568.

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The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and da

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Voineskos, Aristotle N., L. J. O’Donnell, N. J. Lobaugh, et al. "QUANTITATIVE EXAMINATION OF A NOVEL CLUSTERING METHOD USING MAGNETIC RESONANCE DIFFUSION TENSOR TRACTOGRAPHY." Clinical & Investigative Medicine 31, no. 4 (2008): 24. http://dx.doi.org/10.25011/cim.v31i4.4830.

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Introduction: MR diffusion tensor imaging (DTI) is the most powerful and currentlythe only way to visualize the organization of white matter fiber tracts in vivo. As this is a relatively newimaging technique, new tools are developed for quantifying fiber tracts, andrequire evaluation. We examined scalar indices of the diffusion tensor with two different tractography methods. We compared a novel clustering approach with a multiple region of interest (MROI) approach in a healthy and disease (schizophrenia) population. Methods: DTI images were acquired in 12 participants (n=6 patients withschizop

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Tanveer Baig, Z., and Chandrasekar Shastry. "Smart Hybridized Routing Protocol for Animal Monitoring and Tracking Applications." Scalable Computing: Practice and Experience 23, no. 4 (2022): 339–49. http://dx.doi.org/10.12694/scpe.v23i4.2040.

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Wireless sensor networks (WSN) have been exploited for {countless} application domains, most notably the surveillance of environments and habitats, which has already become a critical mission. As a result, WSNs have been implemented to monitor animal care and track their health status. However, excessive energy utilization and communication traffic on packet transmissions lead to system deterioration, especially whenever perceived information captured in the monitoring area is transferred to the access point over multiple dynamic sinks. Further to manage the energy and data transmission issue,

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Priyambada, Satrio Adi, Mahendrawathi ER, and Bernardo Nugroho Yahya. "Curriculum Assessment of Higher Educational Institution Using Trace-segmented Clustering." Jurnal Teknik Industri 20, no. 1 (2018): 33–48. http://dx.doi.org/10.9744/jti.20.1.33-48.

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Curriculum mining is research area that assess students’ learning behavior and compare it with the curriculum guideline. Previous work developed sequence matching alignment approach to check the conformance between students’ learning behavior and curriculum guideline. Considering only the sequence matching alignment is insufficient to understand the patterns of group of students. Another work proposed an approach by aggregating the students’ profile to represent students’ learning behavior and investigate the impact of the learning behavior to their learning performance. However, the aggregate

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Sheng, Ming-Jie, Hao-Ran Yu, Sijia Li, et al. "Baryonic Effects on Lagrangian Clustering and Angular Momentum Reconstruction." Astrophysical Journal 943, no. 2 (2023): 128. http://dx.doi.org/10.3847/1538-4357/acae92.

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Abstract Recent studies illustrate the correlation between the angular momenta of cosmic structures and their Lagrangian properties. However, only baryons are observable and it is unclear whether they reliably trace the cosmic angular momenta. We study the Lagrangian mass distribution, spin correlation, and predictability of dark matter, gas, and stellar components of galaxy–halo systems using IllustrisTNG, and show that the primordial segregations between components are typically small. Their protoshapes are also similar in terms of the statistics of moment of inertia tensors. Under the commo

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Tariq, Zeeshan, Naveed Khan, Darryl Charles, Sally McClean, Ian McChesney, and Paul Taylor. "Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework." Algorithms 13, no. 10 (2020): 244. http://dx.doi.org/10.3390/a13100244.

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Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the p

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Marroquín, Iván Dimitri, Jean-Jules Brault, and Bruce S. Hart. "A visual data-mining methodology for seismic facies analysis: Part 1 — Testing and comparison with other unsupervised clustering methods." GEOPHYSICS 74, no. 1 (2009): P1—P11. http://dx.doi.org/10.1190/1.3046455.

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Seismic facies analysis aims to identify clusters (groups) of similar seismic trace shapes, where each cluster can be considered to represent variability in lithology, rock properties, and/or fluid content of the strata being imaged. Unfortunately, it is not always clear whether the seismic data has a natural clustering structure. Cluster analysis consists of a family of approaches that have significant potential for classifying seismic trace shapes into meaningful clusters. The clustering can be performed using a supervised process (assigning a pattern to a predefined cluster) or an unsupervi

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Guo, Yuhong. "Convex Subspace Representation Learning from Multi-View Data." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 387–93. http://dx.doi.org/10.1609/aaai.v27i1.8565.

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Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We ﬁrst formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual norms, we then show a convex min-max dual formulation with a sparsity inducing trace norm can be obtained. We develop a proximal bundle optimization algorithm to globally solve the min-max optimization pr

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Ankur, Singh Bist*. "A WIDE SCALE SURVEY ON CLUSTERING TECHNIQUES FOR MALWARE DETECTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 307–13. https://doi.org/10.5281/zenodo.546351.

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Use of internet has enhanced the problem of malwares. At the same time the advancement in technology has given rise to evolution of portable mobile devices. It has become the danger point of day today activities like mails, mobile banking etc. Our purpose is to trace various clustering techniques for malware detection.

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García-Vergara, Cristina, Joseph F. Hennawi, L. Felipe Barrientos, and Fabrizio Arrigoni Battaia. "Clustering of galaxies around quasars at z ∼ 4." Proceedings of the International Astronomical Union 15, S352 (2019): 171–73. http://dx.doi.org/10.1017/s1743921319009396.

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AbstractWe conduct a survey for Lyman break galaxies (LBGs) and Lyman alpha emitters (LAEs) in the environs of six and 17 z ∼ 4 quasars respectively, probing scales of R≲9h−1Mpc. We detect an enhancement of galaxies (both LBGs and LAEs) in quasar fields, a positive and strong quasar-galaxy cross-correlation function, consistent with a power-law shape, and a strong galaxy auto-correlation function in quasar fields. The three mentioned results are all indicators that quasars trace massive dark matter halos in the early universe.

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TANG, Yahui, Tong LI, Rui ZHU, Cong LIU, and Shuaipeng ZHANG. "A Hybrid Genetic Service Mining Method Based on Trace Clustering Population." IEICE Transactions on Information and Systems E105.D, no. 8 (2022): 1443–55. http://dx.doi.org/10.1587/transinf.2021edp7190.

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Hou, Chenping. "Learning a subspace for face image clustering via trace ratio criterion." Optical Engineering 48, no. 6 (2009): 060501. http://dx.doi.org/10.1117/1.3149850.

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van Leeuwen, Daphne, Joost Bosman, and Elenna Dugundji. "Spatio-Temporal Clustering of Time-Dependent Origin-Destination Electronic Trace Data." Procedia Computer Science 130 (2018): 359–67. http://dx.doi.org/10.1016/j.procs.2018.04.053.

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Navamani, C., and S. Naveen. "Network Attacks Detection Based on Multi Clustering and Trace back Methods." International Journal of Engineering Trends and Technology 33, no. 2 (2016): 55–60. http://dx.doi.org/10.14445/22315381/ijett-v33p212.

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Lv, Jie, Chengzhuo Lu, Minjun Ye, Yuting Long, Wenbing Li, and Minglong Yang. "Enhanced Landslide Visualization and Trace Identification Using LiDAR-Derived DEM." Sensors 25, no. 14 (2025): 4391. https://doi.org/10.3390/s25144391.

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In response to the inability of traditional remote sensing technology to accurately capture the micro-topographic features of landslide surfaces in vegetated areas under complex terrain conditions, this paper proposes a method for enhanced landslide terrain display and trace recognition based on airborne LiDAR technology. Firstly, a high-precision LiDAR-DEM is constructed using preprocessed LiDAR point cloud data, and visual images are generated using visualization methods, including hillshade, slope, openness, and Sky View Factor (SVF). Secondly, pixel-level image fusion methods are applied t

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Malik, Rio Andika, Sarjon Defit, and Yuhandri Yuhandri. "Comparison of K-Means Clustering Algorithm with Fuzzy C-Means In Measuring Satisfaction Level Of Television Da'wah Surau TV." Rabit : Jurnal Teknologi dan Sistem Informasi Univrab 3, no. 1 (2018): 10–21. http://dx.doi.org/10.36341/rabit.v3i1.387.

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Da'wah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify an

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Honma, Tomoyuki, David W. Saxey, and Simon P. Ringer. "Effect of Trace Addition of Sn in Al-Cu Alloy." Materials Science Forum 519-521 (July 2006): 203–8. http://dx.doi.org/10.4028/www.scientific.net/msf.519-521.203.

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The trace addition of Sn (0.01 at.%) to an Al-1.7Cu (at.%) alloy has been investigated using atom probe tomography (APT), transmission electron microscopy (TEM) and high resolution transmission electron microscope (HRTEM). We have studied samples in the as-quenched (AQ) condition and following ageing at both 160 and 200 °C for very short ageing times so as to better understand the early stages of the decomposition processes. Our data reveal independent Cu-Cu and Sn-Sn clusters in the AQ condition, though we did not observe Cu-Sn clustering. We observed for the first time that some of these ini

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Sureshkumar, U., A. Durkalec, A. Pollo, et al. "Galaxy and Mass Assembly (GAMA)." Astronomy & Astrophysics 653 (September 2021): A35. http://dx.doi.org/10.1051/0004-6361/202140500.

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Context. Galaxies are biased tracers of the underlying network of dark matter. The strength of this bias depends on various galaxy properties and on redshift. One of the methods used to study these dependences of the bias is measurement of galaxy clustering. Such studies are made using galaxy samples from various catalogues, which frequently bear their own problems related to sample selection methods. It is therefore crucial to understand how sample choice influences clustering measurements and which galaxy property is the most direct tracer of the galaxy environment. Aims. We investigate how

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Jagodić, Jovana, Slađan Pavlović, Slavica Borković-Mitić, et al. "Examination of Trace Metals and Their Potential Transplacental Transfer in Pregnancy." International Journal of Molecular Sciences 23, no. 15 (2022): 8078. http://dx.doi.org/10.3390/ijms23158078.

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With the ever-growing concern for human health and wellbeing, the prenatal period of development requires special attention since fetuses can be exposed to various metals through the mother. Therefore, this study explored the status of selected toxic (Pb, Cd, Ni, As, Pt, Ce, Rb, Sr, U) and essential trace metals (Mn, Co, Cu, Zn, Se) in the umbilical cord (UC) sera, maternal sera, and placental tissue samples of 92 healthy women with normal pregnancies. A further aim focuses on the potential transplacental transfer of these trace metals. Based on the obtained levels of investigated elements in

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de Matos, Marcílio Castro, Paulo Léo Osorio, and Paulo Roberto Johann. "Unsupervised seismic facies analysis using wavelet transform and self-organizing maps." GEOPHYSICS 72, no. 1 (2007): P9—P21. http://dx.doi.org/10.1190/1.2392789.

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Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps (SOMs) as a new way to build seismic facies maps and to estimate the number of seismic facies. In the sec

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Han, Jiwon Jesse, Kareem El-Badry, Scott Lucchini, et al. "Hypervelocity Stars Trace a Supermassive Black Hole in the Large Magellanic Cloud." Astrophysical Journal 982, no. 2 (2025): 188. https://doi.org/10.3847/1538-4357/adb967.

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Abstract Hypervelocity stars (HVSs) are produced by the Hills mechanism when a stellar binary is disrupted by a supermassive black hole (SMBH). The HVS Survey detected 21 unbound B-type main-sequence stars in the Milky Way’s outer halo that are consistent with ejection via the Hills mechanism. We revisit the trajectories of these stars in light of proper motions from Gaia DR3 and modern constraints on the Milky Way–Large Magellanic Cloud (LMC) orbit. We find that half of the unbound HVSs discovered by the HVS Survey trace back not to the Galactic Center but to the LMC. Motivated by this findin

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Torpy, Aaron, Rong Fan, Nicholas Wilson, Colin MacRae, and Peter Austin. "Quantifying Trace Element Variations in Chrysocolla by Clustering FEG-EPMA Hyperspectral Maps." Microscopy and Microanalysis 27, S1 (2021): 1870–72. http://dx.doi.org/10.1017/s1431927621006826.

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Bingol, Kerem, and Rafael Brüschweiler. "Deconvolution of Chemical Mixtures with High Complexity by NMR Consensus Trace Clustering." Analytical Chemistry 83, no. 19 (2011): 7412–17. http://dx.doi.org/10.1021/ac201464y.

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Bui, Hong-Nhung, Quang-Thuy Ha, and Tri-Thanh Nguyen. "A NOVEL SIMILARITY MEASURE FOR TRACE CLUSTERING BASED ON NORMALIZED GOOGLE DISTANCE." JP Journal of Heat and Mass Transfer, Special Issue 3 (August 9, 2018): 341–46. http://dx.doi.org/10.17654/hmsi318341.

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Song, M., H. Yang, S. H. Siadat, and M. Pechenizkiy. "A comparative study of dimensionality reduction techniques to enhance trace clustering performances." Expert Systems with Applications 40, no. 9 (2013): 3722–37. http://dx.doi.org/10.1016/j.eswa.2012.12.078.

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Hamasuna, Yukihiro, Ryo Ozaki, and Yasunori Endo. "Two-Stage Clustering Based on Cluster Validity Measures." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 1 (2018): 54–61. http://dx.doi.org/10.20965/jaciii.2018.p0054.

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To handle a large-scale object, a two-stage clustering method has been previously proposed. The method generates a large number of clusters during the first stage and merges clusters during the second stage. In this paper, a novel two-stage clustering method is proposed by introducing cluster validity measures as the merging criterion during the second stage. The significant cluster validity measures used to evaluate cluster partitions and determine the suitable number of clusters act as the criteria for merging clusters. The performance of the proposed method based on six typical indices is c

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Mattsson, L., J. P. U. Fynbo, and B. Villarroel. "Small-scale clustering of nano-dust grains in supersonic turbulence." Monthly Notices of the Royal Astronomical Society 490, no. 4 (2019): 5788–97. http://dx.doi.org/10.1093/mnras/stz2957.

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ABSTRACT We investigate the clustering and dynamics of nano-sized particles (nano dust) in high-resolution (10243) simulations of compressible isothermal hydrodynamic turbulence. It is well established that large grains will decouple from a turbulent gas flow, while small grains will tend to trace the motion of the gas. We demonstrate that nano-sized grains may cluster in a turbulent flow (fractal small-scale clustering), which increases the local grain density by at least a factor of a few. In combination with the fact that nano-dust grains may be abundant in general, and the increased intera

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Marroquín, Iván Dimitri, Jean-Jules Brault, and Bruce S. Hart. "A visual data-mining methodology for seismic facies analysis: Part 2 — Application to 3D seismic data." GEOPHYSICS 74, no. 1 (2009): P13—P23. http://dx.doi.org/10.1190/1.3046456.

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A visual data-mining approach to unsupervised clustering analysis can be an effective tool for visualizing and understanding patterns inherent in seismic data (i.e., seismic facies). The unsupervised clustering analysis is completely data-driven, requiring no external information (e.g., well logs) to guide the seismic-trace classification. We demonstrate the application of the visual data-mining approach to seismic facies analysis on a real 3D seismic data volume. We select two stratigraphic intervals, the first including a Devonian pinnacle reef system and the second containing a Jurassic sil

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Yu, Tianxiang, Yang Xin, Yuexin Tao, Bingqing Hou, and Hongliang Zhu. "Network Communication Protocol Reverse Engineering Based on Auto-Encoder." Security and Communication Networks 2022 (October 6, 2022): 1–14. http://dx.doi.org/10.1155/2022/2924479.

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Network communication protocol reverse engineering is useful for network security, including protocol fuzz testing, botnet command infiltration, and service script generation. Many models have been proposed to generate field boundary, field semantic, state machine, and some other format information from network trace and program execution for text-based protocol and hybrid protocols. However, how to extract format information from network trace data for binary-based protocol still remains a challenging issue. Existing network-trace-based models focus on text-based and hybrid protocols, using t

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S, Sumaiya Sheerin, and Jayalakshmi V. "Analysis of Clustering Algorithm in Securing VANETs." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 200. http://dx.doi.org/10.23956/ijermt.v6i8.140.

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Security of wired and wireless networks is the most challengeable in today’s computer world. Computer and the development of network technology and its application cause the decrease in the Security of the system. It improves the attention of the people because once the data has been destroyed; it is a tedious process to recollect the lost data which causes the interrupt in the network and the performance. The main goal of VANET is to disseminate safety messages from Source to destination without negotiating security. The nodes in VANET is rich in mobility henceforth, there are many challenges

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LIN, JA-CHEN. "MULTI-CLASS CLUSTERING BY ANALYTICAL TWO-CLASS FORMULAS." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 04 (1996): 307–23. http://dx.doi.org/10.1142/s0218001496000220.

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This paper proposes a new clustering method based on the hierarchical use of the analytical two-class clustering tool introduced by Lin and Tsai.1 The method comprises two phases. In the first phase, called the splitting phase, the data set is hierarchically decomposed into some subsets. In the second phase, called the merging phase, the set-to-set distances between these subsets are checked so that some subsets can be merged back together to obtain better clustering results. We use the idea of the so-called dense cut to determine when to stop the splitting phase. We also use a trace-following

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Prathama, Frans, Bernardo Nugroho Yahya, Danny Darmawan Harjono, and Mahendrawathi ER. "Trace Clustering Exploration for Detecting Sudden Drift: A Case Study in Logistic Process." Procedia Computer Science 161 (2019): 1122–30. http://dx.doi.org/10.1016/j.procs.2019.11.224.

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ZHANG, Hong-xin, Jing LI, Han GAN, and Peng-fei HE. "RETRACTED: A study on clustering-based power trace classification for side channel attacks." Journal of China Universities of Posts and Telecommunications 21 (July 2014): 101–5. http://dx.doi.org/10.1016/s1005-8885(14)60505-7.

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Apostolo, Guilherme Henrique, Flavia Bernardini, Luiz C. Schara Magalhães, and Débora C. Muchaluat-Saade. "eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine Learning." Energies 15, no. 2 (2022): 462. http://dx.doi.org/10.3390/en15020462.

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As wireless local area networks grow in size to provide access to users, power consumption becomes an important issue. Power savings in a large-scale Wi-Fi network, with low impact to user service, is undoubtedly desired. In this work, we propose and evaluate the eSCIFI energy saving mechanism for Wireless Local Area Networks (WLANs). eSCIFI is an energy saving mechanism that uses machine learning algorithms as occupancy demand estimators. The eSCIFI mechanism is designed to cope with a broader range of WLANs, which includes Wi-Fi networks such as the Fluminense Federal University (UFF) SCIFI

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Koswara, Asep. "Geospatial analysis of Islamic business trends: Mapping global interest via google search query clustering." Alkasb: Journal of Islamic Economics 4, no. 1 (2025): 73–98. https://doi.org/10.59005/alkasb.v4i1.632.

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The Islamic economy, encompassing sectors such as finance, halal food, tourism, and fintech, has expanded rapidly worldwide in recent years. Yet, global public interest trends in these sectors remain underexplored, particularly from a digital behavior standpoint. This study investigates Islamic business trends by analyzing and clustering Google search data (2020–2025) to uncover geographic and thematic interest patterns. Using geospatial mapping and unsupervised machine learning, it evaluates Google Trends data to reveal temporal and spatial shifts in engagement. Findings show Islamic finance

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Handini, Esti, I. Wayan Warmada, Toshiaki Hasenaka, Nicholas D. Barber, and Tomoyuki Shibata. "Geochemistry of arc alkaline magmatism of Java Island, Sunda Arc: a statistical review." IOP Conference Series: Earth and Environmental Science 1071, no. 1 (2022): 012013. http://dx.doi.org/10.1088/1755-1315/1071/1/012013.

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Abstract Alkaline magmatism develops in the rear arc area of Java, Sunda Arc, at different range of slab depth; ~270-580 km in central section and ~150 km in the east. We collate published geochemical data of volcanic rocks from four alkaline volcanoes (Muria, Lasem, Bawean, and Ringgit-Beser) and perform statistical analysis to evaluate geochemical characteristics of each suite. A set of major and trace elements is scaled and transformed using principal component analysis (PCA) and then followed by implementation of k-means algorithm to cluster the data points based on Euclidian distances. K-

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Et. al., Mr Pradeep K. G. M. "Energy Efficient Scheduling Algorithm for Structural Health Building Monitoring System(Shbm) to Increase the Battery Lifetime." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5005–12. http://dx.doi.org/10.17762/turcomat.v12i3.2014.

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A structural health building monitoring system is invented, achieved and attempted using Wireless Sensor Networks .Ambient vibration of the structure is audited and is implemented to identify the health status of the structure. With WSN, low cost monitoring is possible without intruding with the action of the structure. In this cluster head is heavily loaded since it gathers information from all other member nodes and integrate then and forward to sink either directly or through intermediate other cluster head. I near future cluster head drain and we need to select another cluster head via re

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Swapnali, Sonawane *. Prof.D.S.Kulkarni. "CONSTRUCTION OF IMPROVED PROCESS MODELS BY CLUSTERING EVENT LOGS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 216–521. https://doi.org/10.5281/zenodo.829757.

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One main motive is to solve the problem that presently exist in process discovery, which includes unable to search out correct and understandable process models out of event logs stemming from exceptionally flexible environments. Programming analyst spend extra time in dealing with programming bugs. An unavoidable stride of fixing bugs is bug processing in a system, because of this to efficaciously relegate a designer to every other bug. To decrease the time cost in manual work, content classifications are linked to lead programmed bug processing. This system addresses the difficulty of statis

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