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Dissertations / Theses on the topic 'Data stream mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Tong, Suk-man Ivy. "Techniques in data stream mining." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34737376.

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Tong, Suk-man Ivy, and 湯淑敏. "Techniques in data stream mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34737376.

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3

Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.

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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.

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4

Kranen, Philipp [Verfasser]. "Anytime algorithms for stream data mining / Philipp Kranen." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2011. http://d-nb.info/1018257942/34.

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5

Boedihardjo, Arnold Priguna. "Efficient Algorithms for Mining Data Streams." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28686.

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Data streams are ordered sets of values that are fast, continuous, mutable, and potentially unbounded. Examples of data streams include the pervasive time series which span domains such as finance, medicine, and transportation. Mining data streams require approaches that are efficient, adaptive, and scalable. For several stream mining tasks, knowledge of the data's probability density function (PDF) is essential to deriving usable results. Providing an accurate model for the PDF benefits a variety of stream mining applications and its successful development can have far-reaching impact to the

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6

Meng, Yu. "Extensible Markov model an efficient data mining framework for spatiotemporal stream data /." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3258040.

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Thesis (Ph.D. in Computer Science)--S.M.U., 2007.<br>Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1732. Adviser: Margaret H. Dunham. Includes bibliographical references.

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7

Wang, Dan Tong. "Outlier detection with data stream mining approach in high-dimenional time series data." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691091.

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8

Mumtaz, Ali. "Window-based stream data mining for classification of Internet traffic." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27601.

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Accurate classification of Internet applications is a fundamental requirement for network provisioning, network security, maintaining quality of services and network management. Increasingly, new applications are being introduced on the Internet. The traffic volume and patterns of some of the new applications such as Peer-to-Peer (P2P) file sharing put pressure on service providers' networks in terms of congestion and delay, to the point that maintaining Quality of Services (QoS) planned in the access network requires the provisioning of additional bandwidth sooner than planned. Peer-to-Peer a

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9

Silvestri, Claudio <1974&gt. "Distributed and stream data mining algorithms for frequent pattern discovery." Doctoral thesis, Università Ca' Foscari Venezia, 2006. http://hdl.handle.net/10579/143.

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10

Hung, Yee-shing Regant, and 洪宜成. "The complexities of tracking quantiles and frequent items in a data stream." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B41758183.

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Hung, Yee-shing Regant. "The complexities of tracking quantiles and frequent items in a data stream." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B41758183.

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12

Lee, Lap-kei, and 李立基. "New results on online job scheduling and data stream algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42182451.

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Lee, Lap-kei. "New results on online job scheduling and data stream algorithms." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42182451.

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14

Zhang, Wen, and 张问. "Design and analysis of efficient algorithms for finding frequent itemsin a data stream." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45702597.

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15

Seyfi, Majid. "Mining discriminative itemsets in data streams using different window models." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/120850/1/Majid_Seyfi_Thesis.pdf.

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Big data availability in areas such as social networks, online marketing systems and stock markets is a good source for knowledge discovery. This thesis studies how discriminative itemsets can be discovered in the data streams made of transactions out of user profiles. Discriminative itemsets are frequent in one data stream with much higher frequencies than same itemsets in other data streams in the application domain. This research uses heuristics to manage the large and complex datasets by decreasing the number of candidate patterns. This gives researchers a better understanding of pattern m

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Dam, Hai Huong Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.

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Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the

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Tschumitschew, Katharina [Verfasser], and Frank [Akademischer Betreuer] Klawonn. "Statistical and Probabilistic Methods for Data Stream Mining / Katharina Tschumitschew ; Betreuer: Frank Klawonn." Braunschweig : Technische Universität Braunschweig, 2012. http://d-nb.info/1175823864/34.

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Carvalho, Danilo Codeco. "Obtenção de padrões sequenciais em data streams atendendo requisitos do Big Data." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8280.

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Submitted by Daniele Amaral (daniee_ni@hotmail.com) on 2016-10-20T18:13:56Z No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:36Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:42Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Made available

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García-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.

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The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data s

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Ma, Bin Bin. "Data stream mining in fog computing environment with feature selection using ensemble of swarm search algorithms." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950659.

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Iyer, Vasanth. "Ensemble Stream Model for Data-Cleaning in Sensor Networks." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/973.

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Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the

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Nunes, André Luís. "Um estudo investigativo de algoritmos de regressão para data streams." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6345.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-13T14:22:04Z No. of bitstreams: 1 André Luís Nunes_.pdf: 2523682 bytes, checksum: 5e3899cfac6d76db6b2c6ac16b7f5325 (MD5)<br>Made available in DSpace on 2017-06-13T14:22:04Z (GMT). No. of bitstreams: 1 André Luís Nunes_.pdf: 2523682 bytes, checksum: 5e3899cfac6d76db6b2c6ac16b7f5325 (MD5) Previous issue date: 2017-03-28<br>Nenhuma<br>A explosão no volume de dados e a sua velocidade de expansão tornam as tarefas de descoberta do conhecimento e a análise de dados desafiantes, ainda mais quando consideradas bases não-estacionárias

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23

Addimando, Alessio. "Progettazione di un intrusion detection system su piattaforma big data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16755/.

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Negli ultimi anni, nel panorama digitale, è stato rilevato un ingente aumento del numero di dispositivi e utenti con accesso ad Internet. Proporzionalmente a questi fattori ogni giorno vengono generati continuamente, e in qualsiasi contesto, grandi quantità di dati difficili da gestire. Questo ha fatto emergere la necessità di riorganizzare gli asset aziendali per far fronte ad un calibro di informazione maggiore e per far in modo che la gestione stessa ne estragga valore concreto per la realtà decisionale. L'insieme di queste motivazioni da vita al fenomeno dei Big Data. Affiancato a ques

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24

Ostovar, Alireza. "Business process drift: Detection and characterization." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127157/1/Alireza_Ostovar_Thesis.pdf.

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This research contributes a set of techniques for the early detection and characterization of process drifts, i.e. statistically significant changes in the behavior of business operations, as recorded in transactional data. Early detection and subsequent characterization of process drifts allows organizations to take prompt remedial actions and avoid potential repercussions resulting from unplanned changes in the behavior of their operations.

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25

Song, Ge. "Méthodes parallèles pour le traitement des flux de données continus." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC059/document.

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Nous vivons dans un monde où une grande quantité de données est généré en continu. Par exemple, quand on fait une recherche sur Google, quand on achète quelque chose sur Amazon, quand on clique en ‘Aimer’ sur Facebook, quand on upload une image sur Instagram, et quand un capteur est activé, etc., de nouvelles données vont être généré. Les données sont différentes d’une simple information numérique, mais viennent dans de nombreux format. Cependant, les données prisent isolément n’ont aucun sens. Mais quand ces données sont reliées ensemble on peut en extraire de nouvelles informations. De plus,

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26

Zhang, Liangwei. "Big Data Analytics for Fault Detection and its Application in Maintenance." Doctoral thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60423.

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Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems a

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27

Sýkora, Petr. "Dolování v proudu dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236766.

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This thesis deals with the data mining in data stream which represents fast developing area of information technology. The text describes common principles of data mining, explains what data stream is and shows methods for its preprocessing and algorithms for following data mining. The special attention is given to the VFDT and the CVDT algorithm. The next mentioned are the spatiotemporal data and related data mining. The second part describes the design and implementation of the application for classification over spatiotemporal data stream represented by road traffic data and following predi

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28

Pesaranghader, Ali. "A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38190.

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Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so cause dangerous consequences, e.g., putting people lives at stake, if no reaction is adopted. Therefore, learning systems need to apply intelligent algorithms to monitor evolvement in their environments and update themselves effectively. Further, we may experience fluctuations regarding the p

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29

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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30

De, Alburquerque Melo Cassio. "Real-time Distributed Computation of Formal Concepts and Analytics." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00966184.

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The advances in technology for creation, storage and dissemination of data have dramatically increased the need for tools that effectively provide users with means of identifying and understanding relevant information. Despite the great computing opportunities distributed frameworks such as Hadoop provide, it has only increased the need for means of identifying and understanding relevant information. Formal Concept Analysis (FCA) may play an important role in this context, by employing more intelligent means in the analysis process. FCA provides an intuitive understanding of generalization and

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David, Lukáš. "Dolování v prostřední MS SQL pomocí inkrementálních algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236484.

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This work deals with issues in data streams mining which nowadays is a very dynamic area in information technology. The thesis describes the general principles of data mining. There are also the principles of data mining in the data streams. Special attention is given to the implemented algorithm CluStream. In the practical part the data stream processing solution was designed and implemented by the MSSQL technology using the above algorithm. The functionality of the algorithm was verified using own data stream generator.

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Wu, Burton. "New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/46084/1/Burton_Wu_Thesis.pdf.

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This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell towe

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Paavola, M. (Marko). "An efficient entropy estimation approach." Doctoral thesis, Oulun yliopisto, 2011. http://urn.fi/urn:isbn:9789514295935.

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Abstract Advances in miniaturisation have led to the development of new wireless measurement technologies such as wireless sensor networks (WSNs). A WSN consists of low cost nodes, which are battery-operated devices, capable of sensing the environment, transmitting and receiving, and computing. While a WSN has several advantages, including cost-effectiveness and easy installation, the nodes suffer from small memory, low computing power, small bandwidth and limited energy supply. In order to cope with restrictions on resources, data processing methods should be as efficient as possible. As a re

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34

Putina, Andrian. "Unsupervised anomaly detection : methods and applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT012.

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Une anomalie (également connue sous le nom de outlier) est une instance qui s'écarte de manière significative du reste des données et est définie par Hawkins comme "une observation, qui s'écarte tellement des autres observations qu'elle éveille les soupçons qu'il a été généré par un mécanisme différent". La détection d’anomalies (également connue sous le nom de détection de valeurs aberrantes ou de nouveauté) est donc le domaine de l’apprentissage automatique et de l’exploration de données dans le but d’identifier les instances dont les caractéristiques semblent être incohérentes avec le reste

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35

Jiang, Fan. "Frequent pattern mining of uncertain data streams." Springer-Verlag, 2011. http://hdl.handle.net/1993/5233.

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When dealing with uncertain data, users may not be certain about the presence of an item in the database. For example, due to inherent instrumental imprecision or errors, data collected by sensors are usually uncertain. In various real-life applications, uncertain databases are not necessarily static, new data may come continuously and at a rapid rate. These uncertain data can come in batches, which forms a data stream. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, algorithms have been developed to use the sliding window model for processing and

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Snowsill, Tristan. "Data mining in text streams using suffix trees." Thesis, University of Bristol, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556708.

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Data mining in text streams, or text stream mining, is an increasingly im- portant topic for a number of reasons, including the recent explosion in the availability of textual data and an increasing need for people and organi- sations to process and understand as much of that information as possible, from single users to multinational corporations and governments. In this thesis we present a data structure based on a generalised suffix tree which is capable of solving a number of text stream mining tasks. It can be used to detect changes in the text stream, detect when chunks of text are reuse

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Alzghoul, Ahmad. "Mining data streams to increase ‎industrial product availability." Doctoral thesis, Luleå tekniska universitet, Produkt- och produktionsutveckling, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17609.

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Improving product quality is always of industrial interest. Product availability, a function of product maintainability and reliability, is an example of a measurement that can be used to evaluate product quality. Product availability and cost are two units which are especially important to manage in the context of the manufacturing industry, especially where industry is interested in selling or buying offers with increased service content. Industry in general uses different strategies for increasing equipment availability; these include: corrective (immediate or delayed) and preventive strate

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Zhang, Chongsheng. "Managing and mining data streams with evolving tuples." Nice, 2011. http://www.theses.fr/2011NICE4049.

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Depuis environ une décennie, le traitement des flux de données est nécessaire. Pendant ces dix années, les questions liées au traitement de flux de données ont été largement explorées. Récemment, au lieu de proposer des modèles généraux et des algorithmes, le traitement des flux de données s’oriente de plus en plus vers des tâches ou des domaines spécifiques. Dans cette thèse, nous présentons notre étude de la gestion et de la feuille de flux de données avec tuples évolutifs, c'est-à-dire avec des révisions du modèle. Par exemple, dans un système d’enchères en ligne où les offres sur les objet

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Lin, Hong Bill. "Finding frequent itemsets over bursty data streams." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B32046881.

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Lin, Hong Bill, and 林弘. "Finding frequent itemsets over bursty data streams." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B32046881.

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Braik, William. "Détection d'évènements complexes dans les flux d'évènements massifs." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0596/document.

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La détection d’évènements complexes dans les flux d’évènements est un domaine qui a récemment fait surface dans le ecommerce. Notre partenaire industriel Cdiscount, parmi les sites ecommerce les plus importants en France, vise à identifier en temps réel des scénarios de navigation afin d’analyser le comportement des clients. Les objectifs principaux sont la performance et la mise à l’échelle : les scénarios de navigation doivent être détectés en moins de quelques secondes, alorsque des millions de clients visitent le site chaque jour, générant ainsi un flux d’évènements massif.Dans cette thèse

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Yang, Di. "Mining and Managing Neighbor-Based Patterns in Data Streams." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/16.

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The current data-intensive world is continuously producing huge volumes of live streaming data through various kinds of electronic devices, such as sensor networks, smart phones, GPS and RFID systems. To understand these data sources and thus better leverage them to serve human society, the demands for mining complex patterns from these high speed data streams have significantly increased in a broad range of application domains, such as financial analysis, social network analysis, credit fraud detection, and moving object monitoring. In this dissertation, we present a framework to tackle the

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Franke, Conny. "Adaptivity in data stream mining." Diss., 2009. http://proquest.umi.com/pqdweb?did=1983665161&sid=1&Fmt=2&clientId=48051&RQT=309&VName=PQD.

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CHENG, LI-WEI, and 鄭力瑋. "Mining Sequential Patterns in Data Stream." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/56rjbu.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>105<br>Mining sequential pattern is mainly to find the sequential purchasing behaviors for the most customers.in the database. For example, the most customer will buy the product A first and then buy the product B or the product C. We can use the information of the most customers purchasing behavior that we analyzed to make the decision to raise the profit. Mining Sequential Pattern is separated from static and dynamic. Now, we often use dynamic sequential mining. We need to update the data stream that one by one to come. Because the dynamic data stream is update imm

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Guo, Yu-Ting, and 郭育婷. "Mining Frequent Itemsets in Data Stream." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/04586525064400581744.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>99<br>Mining association rules is used to find out frequent itemsets in a given transaction database. However, new transactions will be continuously added into the transaction database, and thus the frequent itemsets will change with time in real applications. For users, they are eager for getting the latest frequent itemsets from the updated database as soon as possible in order to make the best decision. Therefore, it has become an important issue to work out efficient ways of finding the latest frequent itemsets when transactions keep being added to the database.

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46

Sun, Le. "Data stream mining in medical sensor-cloud." Thesis, 2016. https://vuir.vu.edu.au/31032/.

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Data stream mining has been studied in diverse application domains. In recent years, a population aging is stressing the national and international health care systems. Along with the advent of hundreds and thousands of health monitoring sensors, the traditional wireless sensor networks and anomaly detection techniques cannot handle huge amounts of information. Sensor-cloud makes the processing and storage of big sensor data much easier. Sensor-cloud is an extension of Cloud by connecting the Wireless Sensor Networks (WSNs) and the cloud through sensor and cloud gateways, which consiste

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Renda, Alessandro. "Algorithms and techniques for data stream mining." Doctoral thesis, 2021. http://hdl.handle.net/2158/1235915.

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The abstraction of data streams encompasses a vast range of diverse applications that continuously generate data and therefore require dedicated algorithms and approaches for exploitation and mining. In this framework both unsupervised and supervised approaches are generally employed, depending on the task and on the availability of annotated data. This thesis proposes novel algorithms and techniques specifically tailored for the streaming setting and for knowledge discovery from Social Networks. In the first part of this work we propose a novel clustering algorithm for data streams. Our inv

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Hao, Boyu. "Mining frequent itemsets from uncertain data: extensions to constrained mining and stream mining." 2010. http://hdl.handle.net/1993/4034.

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Most studies on frequent itemset mining focus on mining precise data. However, there are situations in which the data are uncertain. This leads to the mining of uncertain data. There are also situations in which users are only interested in frequent itemsets that satisfy user-specified aggregate constraints. This leads to constrained mining of uncertain data. Moreover, floods of uncertain data can be produced in many other situations. This leads to stream mining of uncertain data. In this M.Sc. thesis, we propose algorithms to deal with all these situations. We first design a tree-based mining

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Tao, Yingying. "Mining Time-Changing Data Streams." Thesis, 2011. http://hdl.handle.net/10012/6374.

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Streaming data have gained considerable attention in database and data mining communities because of the emergence of a class of applications, such as financial marketing, sensor networks, internet IP monitoring, and telecommunications that produce these data. Data streams have some unique characteristics that are not exhibited by traditional data: unbounded, fast-arriving, and time-changing. Traditional data mining techniques that make multiple passes over data or that ignore distribution changes are not applicable to dynamic data streams. Mining data streams has been an active research area

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50

Liu, Szu-Yin, and 劉思吟. "Mining Frequent Patterns from Uncertain Data in Data Stream Environment." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/ek3y67.

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碩士<br>國立東華大學<br>資訊工程學系<br>100<br>In the conventional frequent itemsets mining researches, most of the data are precise and static in transaction databases. However, in real applications, such as the information collected in the sensor network, the data may be uncertain and continuous. Therefore, in recent years, mining uncertain data in a data stream environment has become an important research issue. In this thesis, we propose an efficient tree based algorithm to mine frequent uncertain itemsets in a data stream environment. Moreover, a set of experiment is performed to show the benefit of ou

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