Relevant bibliographies by topics / Unsupervised machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Unsupervised machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Unsupervised machine learning"

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d
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S Thakare Jayshri, Vishal. "An Effective Unsupervised Machine Learning Technique and Research Challenges." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 2141–43. http://dx.doi.org/10.21275/sr23523214829.

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Roohi, Adil, Kevin Faust, Ugljesa Djuric, and Phedias Diamandis. "Unsupervised Machine Learning in Pathology." Surgical Pathology Clinics 13, no. 2 (2020): 349–58. http://dx.doi.org/10.1016/j.path.2020.01.002.

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Sibyan, Hidayatus, Wildan Suharso, Edi Suharto, Melda Agnes Manuhutu, and Agus Perdana Windarto. "Optimization of Unsupervised Learning in Machine Learning." Journal of Physics: Conference Series 1783, no. 1 (2021): 012034. http://dx.doi.org/10.1088/1742-6596/1783/1/012034.

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Jha, Ritambhara. "Analyzing Credit Card Consumer Behavior using Unsupervised Machine Learning Techniques." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 460–63. http://dx.doi.org/10.21275/sr24106025150.

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S Nair, Aparna, and Sindhu Daniel. "Customer Segmentation Using K-Means Clustering in Unsupervised Machine Learning." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1376–79. https://doi.org/10.21275/sr25417125301.

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Mounika, D. Venkata, and Mr K. Padmanaban. "Unsupervised Machine Learning For Managing Safety Accidents In Railway Stations." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12240–50. https://doi.org/10.55248/gengpi.6.0525.18149.

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to <span lang="EN-US">improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to val
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Wu, Wei, Jaime Alvarez, Chengcheng Liu, and Hung-Min Sun. "Bot detection using unsupervised machine learning." Microsystem Technologies 24, no. 1 (2016): 209–17. http://dx.doi.org/10.1007/s00542-016-3237-0.

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Meingast, Stefan, Marco Lombardi, and João Alves. "Estimating extinction using unsupervised machine learning." Astronomy & Astrophysics 601 (May 2017): A137. http://dx.doi.org/10.1051/0004-6361/201630032.

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Dissertations / Theses on the topic "Unsupervised machine learning"

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Domingues, Rémi. "Machine Learning for Unsupervised Fraud Detection." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181027.

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Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. Amadeus, a Global Distribution System providing a transaction platform for flight booking by travel agents, is targeted by fraud attempts that could lead to revenue losses and indemnifications. The objective of this thesis is to detect fraud attempts by applying machine learning algorithms to bookings represented by Passenger Name Record history. Due to the lack of labelled data, the current study presents a benchmark o
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Schneider, C. "Using unsupervised machine learning for fault identification in virtual machines." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.

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Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different approaches, or demonstrations that such implementations reduce costs. This thesis compares previous and current self-healing approaches before demonstrating a new, unsupervised approach that combines artificial neural networks with performance tests to perform fault identification in an automated fashion, i.e. the correct and accurate determinati
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Tsang, Wai-Hung. "Kernel methods in supervised and unsupervised learning /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20TSANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.
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Bhaskar, Dhananjay. "Morphology based cell classification : unsupervised machine learning approach." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61342.

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Individual cells adapt their morphology as a function of their differentiation status and in response to environmental cues and selective pressures. While it known that the great majority of these cues and pressures are mediated by changes in intracellular signal transduction, the precise regulatory mechanisms that govern cell shape, size and polarity are not well understood. Systematic investigation of cell morphology involves experimentally perturbing biochemical pathways and observing changes in phenotype. In order to facilitate this work, experimental biologists need software capable of an
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Sidran, David Ezra Segre Alberto Maria. "TIGER an unsupervised machine learning tactical inference generator /." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/319.

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Sidran, David Ezra. "TIGER: an unsupervised machine learning tactical inference generator." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/319.

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We present here TIGER, a Tactical Inference Generator computer program that was designed as a test-bed program for our research, and the results of a series of surveys of Subject Matter Experts (SMEs) testing the following hypotheses: Hypothesis 1: There is agreement among military experts that tactical situations exhibit certain features (or attributes) and that these features can be used by SMEs to group tactical situations by similarity. Hypothesis 2: The best match (by TIGER of a new scenario to a scenario from its historical database) predicts what the experts would choose. We have condu
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Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.

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La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance da
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Roux, Brian. "Reconstructing Textual File Fragments Using Unsupervised Machine Learning Techniques." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/881.

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This work is an investigation into reconstructing fragmented ASCII files based on content analysis motivated by a desire to demonstrate machine learning's applicability to Digital Forensics. Using a categorized corpus of Usenet, Bulletin Board Systems, and other assorted documents a series of experiments are conducted using machine learning techniques to train classifiers which are able to identify fragments belonging to the same original file. The primary machine learning method used is the Support Vector Machine with a variety of feature extractions to train from. Additional work is done in
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Panholzer, Georg. "Identifying Deviating Systems with Unsupervised Learning." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1146.

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<p>We present a technique to identify deviating systems among a group of systems in a</p><p>self-organized way. A compressed representation of each system is used to compute similarity measures, which are combined in an affinity matrix of all systems. Deviation detection and clustering is then used to identify deviating systems based on this affinity matrix.</p><p>The compressed representation is computed with Principal Component Analysis and</p><p>Kernel Principal Component Analysis. The similarity measure between two compressed</p><p>representations is based on the angle between the spaces s
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Renström, Martin, and Timothy Holmsten. "Fraud Detection on Unlabeled Data with Unsupervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230592.

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A common problem in systems handling user interaction was the risk for fraudulent behaviour. As an example, in a system with credit card transactions it could have been a person using a another user's account for purchases, or in a system with advertisment it could be bots clicking on ads. These malicious attacks were often disguised as normal interactions and could be difficult to detect. It was especially challenging when working with datasets that did not contain so called labels, which showed if the data point was fraudulent or not. This meant that there were no data that had previously be
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Books on the topic "Unsupervised machine learning"

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Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unsupervised Machine Learning. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557693.

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Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Springer, 2010.

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H, Fisher Douglas, Pazzani Michael John 1958-, and Langley Pat, eds. Concept formation: Knowledge and experience in unsupervised learning. Morgan Kaufmann Publishers, 1991.

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Aldrich, Chris, and Lidia Auret. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5185-2.

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Aldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer London, 2013.

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Celebi, M. Emre, and Kemal Aydin. Unsupervised Learning Algorithms. Springer London, Limited, 2016.

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Unsupervised Learning Algorithms. Springer, 2016.

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Bakshi, Elisabeth. Unsupervised Learning : Identify Machine Learning Tasks: Machine Learning Course. Independently Published, 2021.

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Unsupervised Learning with R. Packt Publishing, Limited, 2015.

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Unsupervised Learning with R. Packt Publishing, 2015.

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Book chapters on the topic "Unsupervised machine learning"

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Kalita, Jugal. "Unsupervised Learning." In Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003002611-6.

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Geetha, T. V., and S. Sendhilkumar. "Unsupervised Learning." In Machine Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-9.

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Hutter, Marcus. "Unsupervised Learning." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_867.

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Neuer, Marcus J. "Unsupervised Learning." In Machine Learning for Engineers. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-69995-9_5.

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Vermeulen, Andreas François. "Unsupervised Learning: Deep Learning." In Industrial Machine Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5316-8_8.

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Joshi, Ameet V. "Unsupervised Learning." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_14.

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Joshi, Ameet V. "Unsupervised Learning." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12282-8_14.

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Coqueret, Guillaume, and Tony Guida. "Unsupervised learning." In Machine Learning for Factor Investing. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003121596-19.

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Kubat, Miroslav. "Unsupervised Learning." In An Introduction to Machine Learning. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63913-0_14.

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Kubat, Miroslav. "Unsupervised Learning." In An Introduction to Machine Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81935-4_15.

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Conference papers on the topic "Unsupervised machine learning"

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Hossain, Mazharul, Aaron L. Robinson, Lan Wang, and Chrysanthe Preza. "Investigation of unsupervised and supervised hyperspectral anomaly detection." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3029916.

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Kanouté, Mamadou, Edith Grall-Maës, and Pierre Beauseroy. "Unsupervised Feature Selection Using Extreme Learning Machine." In 16th International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013067500003837.

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Karmakar, Chandrabali, Nina Maria Gottschling, Andrés Camero, and Mihai Datcu. "Uncertainty-aware Unsupervised Machine Learning to Draw Coastline." In 2024 Advanced Topics on Measurement and Simulation (ATOMS). IEEE, 2024. https://doi.org/10.1109/atoms60779.2024.10921503.

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Taís Schein, Tatiana, Gustavo Pereira De Almeira, Stephanie Loi Brião, Rodrigo Andrade De Bem, Felipe Gomes De Oliveira, and Paulo L. J. Drews-Jr. "UDBE: Unsupervised Diffusion-Based Brightness Enhancement in Underwater Images." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00096.

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Reite, Aaron A., Scott Kangas, Zackery Steck, George S. Goley, Jonathan Von Stroh, and Steven Forsyth. "Unsupervised feature learning in remote sensing." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2529791.

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Rajpoot, N., M. Arif, and A. H. Bhalerao. "Unsupervised Learning of Shape Manifolds." In British Machine Vision Conference 2007. British Machine Vision Association, 2007. http://dx.doi.org/10.5244/c.21.90.

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Inoue, Tomoya, Yujin Nakagawa, Ryota Wada, et al. "Early Stuck Detection Using Supervised and Unsupervised Machine Learning Approaches." In Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31376-ms.

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Abstract The early detection of a stuck pipe event is crucial as it is one of the major incidents resulting in nonproductive time. An ordinary supervised machine learning approach has been adopted to achieve the detection of stuck pipe in some previous studies. However, for early detection before stuck occurs with this approach, there are challenging issues such as limited stuck pipe data, various causes of stuck, and the lack of a prior exact "stuck sign" which should be a label in the training dataset. In this study, the surface drilling data is first collected from multiple agencies to enha
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Alnutefy, Suliman, and Ali Alsuwayh. "Unsupervised Anomaly Detection." In 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140210.

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This research focuses on Unsupervised Anomaly Detection using the "ambient_temperature_system_failure.csv" dataset from Numenta Anomaly Benchmark (NAB). The dataset contains time-series temperature readings from an industrial machine's sensor. The aim is to detect anomalies indicating system failures or aberrant behavior without labeled data. Various algorithms, such as K-means, Gaussian/Elliptic Envelopes, Markov Chain, Isolation Forest, One-Class SVM, and RNNs, are applied to analyze the temperature data. These algorithms are chosen for their ability to identify significant deviations in unl
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Hongeng, S. "Unsupervised Learning of Multi-Object Events." In British Machine Vision Conference 2004. British Machine Vision Association, 2004. http://dx.doi.org/10.5244/c.18.51.

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PULAKURTHI, PRASANNA REDDY, Sohail Dianat, Majid Rabbani, Suya You, and Raghuveer M. Rao. "Unsupervised domain adaptation using feature aligned maximum classifier discrepancy." In Applications of Machine Learning 2022, edited by Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2022. http://dx.doi.org/10.1117/12.2646422.

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Reports on the topic "Unsupervised machine learning"

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Vesselinov, Velimir Valentinov. TensorDecompostions : Unsupervised machine learning methods. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493534.

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Moral, Rafael. Introduction to Machine Learning. Instats Inc., 2024. http://dx.doi.org/10.61700/qfxukp14jlpfd1478.

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This comprehensive workshop provides a thorough introduction to machine learning, focusing on both theoretical concepts and practical applications using R. Designed for PhD students, professors, and researchers, it covers essential techniques such as supervised and unsupervised learning, dimension reduction, and tree-based methods, enhancing participants' data analysis skills and research capabilities.
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Vesselinov, Velimir, Bulbul Ahmmed, Maruti Mudunuru, et al. Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1781347.

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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
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Shekhar, Shubhranshu, Jetson Leder-Luis, and Leman Akoglu. Unsupervised Machine Learning for Explainable Health Care Fraud Detection. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w30946.

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Ahmmed, Bulbul. Supervised and Unsupervised Machine Learning to Understanding Reactive-transport Data. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1630844.

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Yeamans, Katelyn Angela. Unsupervised Machine Learning for Evaluation of Aging in Explosive Pressed Pellets. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1484618.

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Wehner, Michael, Mark Risser, Paul Ullrich, and Shiheng Duan. Exploring variability in seasonal average and extreme precipitation using unsupervised machine learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769708.

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Lin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1994110.

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Kim, H. Annual Report for Structure-Aware Unsupervised, Transformational Machine Learning for Drug Discovery. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2438161.

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