Bibliographies thématiques / Data mining

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Littérature scientifique sur le sujet « Data mining »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 28 juin 2025

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Articles de revues sur le sujet "Data mining"

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PYLYPIUK, Tetiana, and Viktor SHCHYRBA. "DATA MINING METHODS." Collection of scientific papers Kamianets-Podilsky Ivan Ohienko National University Pedagogical series 29 (December 14, 2023): 7–10. http://dx.doi.org/10.32626/2307-4507.2023-29.7-10.

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Research is devoted to Data Mining methods. A comparison of classical and mathematical and statistical methods of data analysis was made. One of the variants of correlation analysis method for intelligent data analysis is proposed and described in an argumentative manner. The question of applying different methodologies for Data Mining is actual. Classically, the following methods of knowledge discovery and analysis are offered in Data Mining: classification; regression; forecasting time sequences (series); clustering; association. As mathematical and statistical methods of analysis in applied research, the most of authors offer such methods as: statistical hypothesis testing, regression models construction and research. Since most real models are not amenable to analysis using classical methods, including regression analysis, the authors propose to use correlational analysis method in Data Mining.
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Shah Neha K, Shah Neha K. "Introduction of Data mining and an Analysis of Data mining Techniques." Indian Journal of Applied Research 3, no. 5 (October 1, 2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.

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Rakholiya, Kalpesh R., and Dr Dhaval Kathiriya. "Data Mining for Moving Object Data." Indian Journal of Applied Research 2, no. 3 (October 1, 2011): 111–13. http://dx.doi.org/10.15373/2249555x/dec2012/34.

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Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (December 2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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Загороднюк, П. А. "Data mining in Go." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 4 (January 10, 2022): 161–66. http://dx.doi.org/10.18137/rnu.v9187.21.04.p.161.

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Целью данной статьи является оценка языка программирования Go как инструмента для реализации методов data mining. Для этого проводится анализ задачи классификации и метода k-ближайших соседей, затем предлагается способ программирования данного метода и организации процесс управления и подготовки исходных данных. В заключение на основе проведенной работы делается вывод, насколько Go подходит для решения подобных задач, и есть ли потенциал для реализации остальных методов. The purpose of this article is to evaluate the Go programming language as a tool for implementing data mining methods. To do this, an analysis of the classification problem and the k-nearest neighbors’ algorithm is carried out, then a method is proposed for how this method can be programmed and the process of managing and preparing the initial data can be organized. In conclusion, based on the work carried out, it sums up how well Go is suitable for solving such problems and whether there is potential for the implementation of other methods.
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AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (March 2, 2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Classical methods of gaining knowledge from data sets are statistical methods. In data mining, new methods besides statistical are used. These new methods have their origin in artificial intelligence. They look for unknown and unexpected relations, which can be uncovered by exploring of data in database. In the article, a utilization of modern methods of data mining is described and especially the methods based on neural networks theory are pursued. The advantages and drawbacks of applications of multiplayer feed forward neural networks and Kohonen’s self-organizing maps are discussed. Kohonen’s self-organizing map is the most promising neural data-mining algorithm regarding its capability to visualize high-dimensional data.
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Stoffel, Kilian. "Web + Data Mining = Web Mining." HMD Praxis der Wirtschaftsinformatik 46, no. 4 (August 2009): 6–20. http://dx.doi.org/10.1007/bf03340377.

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M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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Tsuta, Mizuki. "Data Mining." Nippon Shokuhin Kagaku Kogaku Kaishi 64, no. 6 (2017): 334–35. http://dx.doi.org/10.3136/nskkk.64.334.

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Rossini, Luiz Amelio Sodaite, Renan Ricardo de Polli Silva, Eder Carlos Salazar Sotto, and Liriane Soares De Araújo. "DATA MINING." Revista Interface Tecnológica 15, no. 2 (December 30, 2018): 50–59. http://dx.doi.org/10.31510/infa.v15i2.486.

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A Mineração de Dados (Data Mining) deve ser entendida como um conjunto de esforços empregados para a descoberta de padrões de acordo com bases de dados. Dessa maneira, há condições de gerar conhecimento útil para a tomada de decisões, através de algoritmos computacionais que recebem fatos do mundo real (entrada) e devolvem um padrão de comportamento (saída), expresso como modelagem de um perfil. Sendo assim, o objetivo deste artigo é definir a Mineração de Dados e os conceitos inerentes a ela, bem como elencar algumas ferramentas utilizadas para extração de conhecimento a partir dos dados. A metodologia do trabalho consiste em levantamento bibliográfico. Espera-se como resultado demonstrar a imprescindibilidade da Mineração de Dados no apoio à decisão nas organizações e contribuir para a produção científica e acadêmica.
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Thèses sur le sujet "Data mining"

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Mrázek, Michal. "Data mining." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400441.

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The aim of this master’s thesis is analysis of the multidimensional data. Three dimensionality reduction algorithms are introduced. It is shown how to manipulate with text documents using basic methods of natural language processing. The goal of the practical part of the thesis is to process real-world data from the internet forum. Posted messages are transformed to the numerical representation, then to two-dimensional space and visualized. Later on, topics of the messages are discovered. In the last part, a few selected algorithms are compared.
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Payyappillil, Hemambika. "Data mining framework." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3807.

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Thesis (M.S.)--West Virginia University, 2005<br>Title from document title page. Document formatted into pages; contains vi, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 64-65).
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Abedjan, Ziawasch. "Improving RDF data with data mining." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7133/.

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Linked Open Data (LOD) comprises very many and often large public data sets and knowledge bases. Those datasets are mostly presented in the RDF triple structure of subject, predicate, and object, where each triple represents a statement or fact. Unfortunately, the heterogeneity of available open data requires significant integration steps before it can be used in applications. Meta information, such as ontological definitions and exact range definitions of predicates, are desirable and ideally provided by an ontology. However in the context of LOD, ontologies are often incomplete or simply not available. Thus, it is useful to automatically generate meta information, such as ontological dependencies, range definitions, and topical classifications. Association rule mining, which was originally applied for sales analysis on transactional databases, is a promising and novel technique to explore such data. We designed an adaptation of this technique for min-ing Rdf data and introduce the concept of “mining configurations”, which allows us to mine RDF data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. To this end, we present rule-based approaches for auto-completion, data enrichment, ontology improvement, and query relaxation. Auto-completion remedies the problem of inconsistent ontology usage, providing an editing user with a sorted list of commonly used predicates. A combination of different configurations step extends this approach to create completely new facts for a knowledge base. We present two approaches for fact generation, a user-based approach where a user selects the entity to be amended with new facts and a data-driven approach where an algorithm discovers entities that have to be amended with missing facts. As knowledge bases constantly grow and evolve, another approach to improve the usage of RDF data is to improve existing ontologies. Here, we present an association rule based approach to reconcile ontology and data. Interlacing different mining configurations, we infer an algorithm to discover synonymously used predicates. Those predicates can be used to expand query results and to support users during query formulation. We provide a wide range of experiments on real world datasets for each use case. The experiments and evaluations show the added value of association rule mining for the integration and usability of RDF data and confirm the appropriateness of our mining configuration methodology.<br>Linked Open Data (LOD) umfasst viele und oft sehr große öffentlichen Datensätze und Wissensbanken, die hauptsächlich in der RDF Triplestruktur bestehend aus Subjekt, Prädikat und Objekt vorkommen. Dabei repräsentiert jedes Triple einen Fakt. Unglücklicherweise erfordert die Heterogenität der verfügbaren öffentlichen Daten signifikante Integrationsschritte bevor die Daten in Anwendungen genutzt werden können. Meta-Daten wie ontologische Strukturen und Bereichsdefinitionen von Prädikaten sind zwar wünschenswert und idealerweise durch eine Wissensbank verfügbar. Jedoch sind Wissensbanken im Kontext von LOD oft unvollständig oder einfach nicht verfügbar. Deshalb ist es nützlich automatisch Meta-Informationen, wie ontologische Abhängigkeiten, Bereichs-und Domänendefinitionen und thematische Assoziationen von Ressourcen generieren zu können. Eine neue und vielversprechende Technik um solche Daten zu untersuchen basiert auf das entdecken von Assoziationsregeln, welche ursprünglich für Verkaufsanalysen in transaktionalen Datenbanken angewendet wurde. Wir haben eine Adaptierung dieser Technik auf RDF Daten entworfen und stellen das Konzept der Mining Konfigurationen vor, welches uns befähigt in RDF Daten auf unterschiedlichen Weisen Muster zu erkennen. Verschiedene Konfigurationen erlauben uns Schema- und Wertbeziehungen zu erkennen, die für interessante Anwendungen genutzt werden können. In dem Sinne, stellen wir assoziationsbasierte Verfahren für eine Prädikatvorschlagsverfahren, Datenvervollständigung, Ontologieverbesserung und Anfrageerleichterung vor. Das Vorschlagen von Prädikaten behandelt das Problem der inkonsistenten Verwendung von Ontologien, indem einem Benutzer, der einen neuen Fakt einem Rdf-Datensatz hinzufügen will, eine sortierte Liste von passenden Prädikaten vorgeschlagen wird. Eine Kombinierung von verschiedenen Konfigurationen erweitert dieses Verfahren sodass automatisch komplett neue Fakten für eine Wissensbank generiert werden. Hierbei stellen wir zwei Verfahren vor, einen nutzergesteuertenVerfahren, bei dem ein Nutzer die Entität aussucht die erweitert werden soll und einen datengesteuerten Ansatz, bei dem ein Algorithmus selbst die Entitäten aussucht, die mit fehlenden Fakten erweitert werden. Da Wissensbanken stetig wachsen und sich verändern, ist ein anderer Ansatz um die Verwendung von RDF Daten zu erleichtern die Verbesserung von Ontologien. Hierbei präsentieren wir ein Assoziationsregeln-basiertes Verfahren, der Daten und zugrundeliegende Ontologien zusammenführt. Durch die Verflechtung von unterschiedlichen Konfigurationen leiten wir einen neuen Algorithmus her, der gleichbedeutende Prädikate entdeckt. Diese Prädikate können benutzt werden um Ergebnisse einer Anfrage zu erweitern oder einen Nutzer während einer Anfrage zu unterstützen. Für jeden unserer vorgestellten Anwendungen präsentieren wir eine große Auswahl an Experimenten auf Realweltdatensätzen. Die Experimente und Evaluierungen zeigen den Mehrwert von Assoziationsregeln-Generierung für die Integration und Nutzbarkeit von RDF Daten und bestätigen die Angemessenheit unserer konfigurationsbasierten Methodologie um solche Regeln herzuleiten.
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Liu, Tantan. "Data Mining over Hidden Data Sources." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343313341.

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Taylor, Phillip. "Data mining of vehicle telemetry data." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/77645/.

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Driving a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. As well as these distractions, the driver may also be overloaded for other reasons, such as dealing with an incident on the road or holding conversations in the car. One solution to this distraction problem is to limit the functionality of in-car devices while the driver is overloaded. This can take the form of withholding an incoming phone call or delaying the display of a non-urgent piece of information about the vehicle. In order to design and build these adaptions in the car, we must first have an understanding of the driver's current level of workload. Traditionally, driver workload has been monitored using physiological sensors or camera systems in the vehicle. However, physiological systems are often intrusive and camera systems can be expensive and are unreliable in poor light conditions. It is important, therefore, to use methods that are non-intrusive, inexpensive and robust, such as sensors already installed on the car and accessible via the Controller Area Network (CAN)-bus. This thesis presents a data mining methodology for this problem, as well as for others in domains with similar types of data, such as human activity monitoring. It focuses on the variable selection stage of the data mining process, where inputs are chosen for models to learn from and make inferences. Selecting inputs from vehicle telemetry data is challenging because there are many irrelevant variables with a high level of redundancy. Furthermore, data in this domain often contains biases because only relatively small amounts can be collected and processed, leading to some variables appearing more relevant to the classification task than they are really. Over the course of this thesis, a detailed variable selection framework that addresses these issues for telemetry data is developed. A novel blocked permutation method is developed and applied to mitigate biases when selecting variables from potentially biased temporal data. This approach is infeasible computationally when variable redundancies are also considered, and so a novel permutation redundancy measure with similar properties is proposed. Finally, a known redundancy structure between features in telemetry data is used to enhance the feature selection process in two ways. First the benefits of performing raw signal selection, feature extraction, and feature selection in different orders are investigated. Second, a two-stage variable selection framework is proposed and the two permutation based methods are combined. Throughout the thesis, it is shown through classification evaluations and inspection of the features that these permutation based selection methods are appropriate for use in selecting features from CAN-bus data.
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Sherikar, Vishnu Vardhan Reddy. "I2MAPREDUCE: DATA MINING FOR BIG DATA." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/437.

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This project is an extension of i2MapReduce: Incremental MapReduce for Mining Evolving Big Data . i2MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data mining results. Finally, the performance of i2MapReduce is analyzed by comparing the resultant graphs.
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Zhang, Nan. "Privacy-preserving data mining." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1080.

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Hulten, Geoffrey. "Mining massive data streams /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6937.

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Büchel, Nina. "Faktorenvorselektion im Data Mining /." Berlin : Logos, 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=019006997&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Shao, Junming. "Synchronization Inspired Data Mining." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-137356.

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Livres sur le sujet "Data mining"

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Xu, Yue, Rosalind Wang, Anton Lord, Yee Ling Boo, Richi Nayak, Yanchang Zhao, and Graham Williams, eds. Data Mining. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8531-6.

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Dulli, Susi, Sara Furini, and Edmondo Peron. Data mining. Milano: Springer Milan, 2009. http://dx.doi.org/10.1007/978-88-470-1163-2.

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Stahlbock, Robert, Sven F. Crone, and Stefan Lessmann, eds. Data Mining. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1280-0.

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Islam, Rafiqul, Yun Sing Koh, Yanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, and Zahidul Islam, eds. Data Mining. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1.

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Boo, Yee Ling, David Stirling, Lianhua Chi, Lin Liu, Kok-Leong Ong, and Graham Williams, eds. Data Mining. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0292-3.

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Nakhaeizadeh, Gholamreza, ed. Data Mining. Heidelberg: Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-642-86094-2.

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Aggarwal, Charu C. Data Mining. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8.

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Runkler, Thomas A. Data Mining. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9353-6.

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Kantardzic, Mehmed. Data Mining. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118029145.

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Le, Thuc D., Kok-Leong Ong, Yanchang Zhao, Warren H. Jin, Sebastien Wong, Lin Liu, and Graham Williams, eds. Data Mining. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1699-3.

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Chapitres de livres sur le sujet "Data mining"

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Freitas, Alex A., and Simon H. Lavington. "Data Mining." In Mining Very Large Databases with Parallel Processing, 41–50. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6_5.

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Rahman, Mirza I., and Robbert P. van Manen. "Data Mining." In Principles and Practice of Pharmaceutical Medicine, 587–600. Oxford, UK: Wiley-Blackwell, 2010. http://dx.doi.org/10.1002/9781444325263.ch44.

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Du, Ke-Lin, and M. N. S. Swamy. "Data Mining." In Neural Networks and Statistical Learning, 747–78. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5571-3_25.

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Chang, George, Marcus J. Healey, James A. M. McHugh, and Jason T. L. Wang. "Data Mining." In Mining the World Wide Web, 67–80. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1639-2_5.

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Pappa, Gisele L., and Alex A. Freitas. "Data Mining." In Natural Computing Series, 17–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02541-9_2.

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Du, Ke-Lin, and M. N. S. Swamy. "Data Mining." In Neural Networks and Statistical Learning, 871–903. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_30.

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Lee, Raymond S. T. "Data Mining." In Artificial Intelligence in Daily Life, 71–118. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7695-9_4.

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Morzy, Tadeusz, and Maciej Zakrzewicz. "Data Mining." In Handbook on Data Management in Information Systems, 487–565. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24742-5_11.

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van der Aalst, Wil. "Data Mining." In Process Mining, 89–121. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49851-4_4.

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Mohan, Chilukuri Krishna. "Data Mining." In Frontiers of Expert Systems, 237–58. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4509-5_9.

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Actes de conférences sur le sujet "Data mining"

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Song, Xiaoli, XiaoTong Wang, and Xiaohua Hu. "Semantic pattern mining for text mining." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840600.

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Agarwal, Shivam. "Data Mining: Data Mining Concepts and Techniques." In 2013 International Conference on Machine Intelligence and Research Advancement (ICMIRA). IEEE, 2013. http://dx.doi.org/10.1109/icmira.2013.45.

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Edelstein, Herb. "Data mining." In the seventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/502512.502517.

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"Data mining." In 2015 International Symposium on Advanced Computing and Communication (ISACC). IEEE, 2015. http://dx.doi.org/10.1109/isacc.2015.7377334.

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DeWaal, Mindy. "Data Mining." In the 46th ACM Technical Symposium. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2676723.2693628.

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Ursyn, Anna. "Data mining." In ACM SIGGRAPH 2004 Art gallery. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1185884.1186011.

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Peñafiel, Myriam, Stefanie Vásquez, Diego Vásquez, Juan Zaldumbide, and Sergio Luján-Mora. "Data Mining and Opinion Mining." In the 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3274250.3274263.

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Yang, Tie-li, Ping-Bai, and Yu-Sheng Gong. "Spatial Data Mining Features between General Data Mining." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.167.

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Ashok, Vikas, and Ravi Mukkamala. "Data mining without data." In the 10th annual ACM workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2046556.2046578.

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"Session C: Dynamic data mining & data stream mining." In 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2016. http://dx.doi.org/10.1109/dsmp.2016.7583553.

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Rapports d'organisations sur le sujet "Data mining"

1

Lee, K., H. Kargupta, B. G. Stafford, K. L. Buescher, and B. Ravindran. Data mining. Office of Scientific and Technical Information (OSTI), December 1998. http://dx.doi.org/10.2172/334314.

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Kramer, Mitchell. Customer Data Mining. Boston, MA: Patricia Seybold Group, May 2004. http://dx.doi.org/10.1571/psgp5-27-04cc.

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Kramer, Mitchell. Data Mining at Work. Boston, MA: Patricia Seybold Group, June 2004. http://dx.doi.org/10.1571/psgp6-10-04cc.

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Brown, David A., John Hirdt, and Michal Herman. Data mining the EXFOR database. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1122776.

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Lu, Xiaomeng, Robert Stambaugh, and Yu Yuan. Anomalies Abroad: Beyond Data Mining. Cambridge, MA: National Bureau of Economic Research, September 2017. http://dx.doi.org/10.3386/w23809.

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Davidson, George S., Jana Strasburg, David Stampf, Lev Neymotin, Carl Czajkowski, Eugene Shine, James Bollinger, et al. Data mining for ontology development. Office of Scientific and Technical Information (OSTI), June 2010. http://dx.doi.org/10.2172/992328.

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Berry, Jonathan W., Vitus Joseph Leung, Cynthia Ann Phillips, Ali Pinar, David Gerald Robinson, Tanya Berger-Wolf, Sanjukta Bhowmick, et al. Statistically significant relational data mining :. Office of Scientific and Technical Information (OSTI), February 2014. http://dx.doi.org/10.2172/1204082.

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Zdonik, Stanley B. Monitoring and Mining Data Streams. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada431589.

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Zdonik, Stan B. Monitoring and Mining Data Streams. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada419707.

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Zhan, Zhijun, and LiWu Chang. Privacy-Preserving Collaborative Data Mining. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada464602.

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