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Dissertations / Theses on the topic 'Keywords: Frequent pattern mining'

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Published: 5 June 2025

Last updated: 15 July 2025

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1

Shang, Xuequn. "SQL based frequent pattern mining." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975449176.

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Yun, Unil. "New approaches to weighted frequent pattern mining." Texas A&M University, 2005. http://hdl.handle.net/1969.1/5003.

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Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns. Many techniques such as depth first/breadth first search, use of tree/other data structures, top down/bottom up traversal and vertical/horizontal formats for frequent pattern mining have been developed. Most frequent pattern mining algorithms use a support measure to prune the combinatorial search space. However, support-based pruning is not enough when taking into consideration the characteristics of real datasets. Additionally, after mi

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Liu, Guimei. "Supporting efficient and scalable frequent pattern mining /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20LIUG.

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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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Vu, Lan. "High performance methods for frequent pattern mining." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3667246.

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<p> Current Big Data era is generating tremendous amount of data in most fields such as business, social media, engineering, and medicine. The demand to process and handle the resulting "big data" has led to the need for fast data mining methods to develop powerful and versatile analysis tools that can turn data into useful knowledge. Frequent pattern mining (FPM) is an important task in data mining with numerous applications such as recommendation systems, consumer market analysis, web mining, network intrusion detection, etc. We develop efficient high performance FPM methods for large-scale

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6

Cederquist, Aaron. "Frequent Pattern Mining among Weighted and Directed Graphs." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

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Jiang, Fan. "Efficient frequent pattern mining from big data and its applications." Springer, 2014. http://hdl.handle.net/1993/32083.

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Frequent pattern mining is an important research areas in data mining. Since its introduction, it has drawn attention of many researchers. Consequently, many algorithms have been proposed. Popular algorithms include level-wise Apriori based algorithms, tree based algorithms, and hyperlinked array structure based algorithms. While these algorithms are popular and beneficial due to some nice properties, they also suffer from some drawbacks such as multiple database scans, recursive tree constructions, or multiple hyperlink adjustments. In the current era of big data, high volumes of a wide varie

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Zhang, Qi. "The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261.

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King, Stuart. "Optimizations and applications of Trie-Tree based frequent pattern mining." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (M. S.)--Michigan State University. Dept. of Computer Science and Engineering, 2006.<br>Title from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 79-80). Also issued in print.

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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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Jin, Ruoming. "New techniques for efficiently discovering frequent patterns." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1121795612.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xvii, 170 p.; also includes graphics. Includes bibliographical references (p. 160-170). Available online via OhioLINK's ETD Center

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MacKinnon, Richard Kyle. "Seeing the forest for the trees: tree-based uncertain frequent pattern mining." Springer International Publishing, 2014. http://hdl.handle.net/1993/31059.

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Many frequent pattern mining algorithms operate on precise data, where each data point is an exact accounting of a phenomena (e.g., I have exactly two sisters). Alas, reasoning this way is a simplification for many real world observations. Measurements, predictions, environmental factors, human error, &ct. all introduce a degree of uncertainty into the mix. Tree-based frequent pattern mining algorithms such as FP-growth are particularly efficient due to their compact in-memory representations of the input database, but their uncertain extensions can require many more tree nodes. I propose new

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Tran, Hoang Tung. "Automatic tag correction in videos : an approach based on frequent pattern mining." Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4028/document.

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Nous présentons dans cette thèse un système de correction automatique d'annotations (tags) fournies par des utilisateurs qui téléversent des vidéos sur des sites de partage de documents multimédia sur Internet. La plupart des systèmes d'annotation automatique existants se servent principalement de l'information textuelle fournie en plus de la vidéo par les utilisateurs et apprennent un grand nombre de "classifieurs" pour étiqueter une nouvelle vidéo. Cependant, les annotations fournies par les utilisateurs sont souvent incomplètes et incorrectes. En effet, un utilisateur peut vouloir augmenter

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Bykowski, Artur Boulicaut Jean-François. "Condensed representations of frequent sets application to descriptive pattern discovery /." Villeurbanne : Doc'INSA, 2004. http://docinsa.insa-lyon.fr/these/pont.php?id=bykowski.

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Thèse doctorat : Informatique : Villeurbanne, INSA : 2002.<br>Thèse rédigée en anglais. En annexe, résumé étendu en français. Le titre en français "Représentations condensées d'ensembles fréquents : application à la découverte de motifs descriptifs" n'apparait pas sur la thèse. Titre provenant de l'écran-titre. Bibliogr. p. 142-150.

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Maden, Engin. "Data Mining On Architecture Simulation." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611635/index.pdf.

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Data mining is the process of extracting patterns from huge data. One of the branches in data mining is mining sequence data and here the data can be viewed as a sequence of events and each event has an associated time of occurrence. Sequence data is modelled using episodes and events are included in episodes. The aim of this thesis work is analysing architecture simulation output data by applying episode mining techniques, showing the previously known relationships between the events in architecture and providing an environment to predict the performance of a program in an architecture before

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Nunna, Shinjini. "Using Association Analysis for Medical Diagnoses." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/scripps_theses/808.

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In order to fully examine the application of association analysis to medical data for the purpose of deriving medical diagnoses, we survey classical association analysis and approaches, the current challenges faced by medical association analysis and proposed solutions, and finally culminate this knowledge in a proposition for the application of medical association analysis to the identification of food intolerance. The field of classical association analysis has been well studied since its introduction in the seminal paper on market basket research in the 1990's. While the theory itself is re

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Alkan, Sertan. "A Distributed Graph Mining Framework Based On Mapreduce." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611588/index.pdf.

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The frequent patterns hidden in a graph can reveal crucial information about the network the graph represents. Existing techniques to mine the frequent subgraphs in a graph database generally rely on the premise that the data can be fit into main memory of the device that the computation takes place. Even though there are some algorithms that are designed using highly optimized methods to some extent, many lack the solution to the problem of scalability. In this thesis work, our aim is to find and enumerate the subgraphs that are at least as frequent as the designated threshold in a given grap

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Lopez, Cueva Patricia. "Debugging Embedded Multimedia Application Execution Traces through Periodic Pattern Mining." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-01006213.

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La conception des systèmes multimédia embarqués présente de nombreux déﬁs comme la croissante complexité du logiciel et du matériel sous-jacent, ou les pressions liées aux délais de mise en marche. L'optimisation du processus de débogage et validation du logiciel peut aider à réduire sensiblement le temps de développement. Parmi les outils de débogage de systèmes embarqués, un puissant outil largement utilisé est l'analyse de traces d'exécution. Cependant, l'évolution des techniques de tra¸cage dans les systèmes embarqués se traduit par des traces d'exécution avec une grande quantité d'informa

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Shatnawi, Safwan. "A data mining approach to ontology learning for automatic content-related question-answering in MOOCs." Thesis, Robert Gordon University, 2016. http://hdl.handle.net/10059/2122.

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The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured document

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Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738/document.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont propo

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Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont propo

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Pragarauskaitė, Julija. "Dažnų sekų analizė sprendimų priėmimui labai didelėse duomenų bazėse." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130701_092337-79289.

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Didžiuliai informacijos kiekiai yra sukaupiami kiekvieną dieną pasaulyje bei jie sparčiai auga. Apytiksliai duomenų tyrybos algoritmai yra labai svarbūs analizuojant tokius didelius duomenų kiekius, nes algoritmų greitis yra ypač svarbus daugelyje sričių, tuo tarpu tikslieji metodai paprastai yra lėti bei naudojami tik uždaviniuose, kuriuose reikalingas tikslus atsakymas. Ši disertacija analizuoja kelias duomenų tyrybos sritis: dažnų sekų paiešką bei vizualizaciją sprendimų priėmimui. Dažnų sekų paieškai buvo pasiūlyti trys nauji apytiksliai metodai, kurie buvo testuojami naudojant tikras b

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Alay, Gokcen. "A Classification System For The Problem Of Protein Subcellular Localization." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608914/index.pdf.

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The focus of this study is on predicting the subcellular localization of a protein. Subcellular localization information is important for protein function annotation which is a fundamental problem in computational biology. For this problem, a classification system is built that has two main parts: a predictor that is based on a feature mapping technique to extract biologically meaningful information from protein sequences and a client/server architecture for searching and predicting subcellular localizations. In the first part of the thesis, we describe a feature mapping technique based o

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Adam, Chloé. "Pattern Recognition in the Usage Sequences of Medical Apps." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC027/document.

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Les radiologues utilisent au quotidien des solutions d'imagerie médicale pour le diagnostic. L'amélioration de l'expérience utilisateur est toujours un axe majeur de l'effort continu visant à améliorer la qualité globale et l'ergonomie des produits logiciels. Les applications de monitoring permettent en particulier d'enregistrer les actions successives effectuées par les utilisateurs dans l'interface du logiciel. Ces interactions peuvent être représentées sous forme de séquences d'actions. Sur la base de ces données, ce travail traite de deux sujets industriels : les pannes logicielles et l'er

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Singh, Shailendra. "Smart Meters Big Data : Behavioral Analytics via Incremental Data Mining and Visualization." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35244.

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The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision making to achieve sustainable energy efficiency. Buying-in consumer confidence through respecting occupants' energy consumption behavior and preferences towards improved participation in various energy programs is imperative but difficult to obtain. The key elements for understanding and predicting household energy consumption are activities occupants perform, appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted f

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Bogorny, Vania. "Enhancing spatial association rule mining in geographic databases." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/7841.

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A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são

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Giudice, Riccardo. "Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9054/.

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Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time. Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.

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Legler, Thomas. "Datenzentrierte Bestimmung von Assoziationsregeln in parallelen Datenbankarchitekturen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-23701.

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Die folgende Arbeit befasst sich mit der Alltagstauglichkeit moderner Massendatenverarbeitung, insbesondere mit dem Problem der Assoziationsregelanalyse. Vorhandene Datenmengen wachsen stark an, aber deren Auswertung ist für ungeübte Anwender schwierig. Daher verzichten Unternehmen auf Informationen, welche prinzipiell vorhanden sind. Assoziationsregeln zeigen in diesen Daten Abhängigkeiten zwischen den Elementen eines Datenbestandes, beispielsweise zwischen verkauften Produkten. Diese Regeln können mit Interessantheitsmaßen versehen werden, welche dem Anwender das Erkennen wichtiger Zusammenh

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Wang, Chao. "Exploiting non-redundant local patterns and probabilistic models for analyzing structured and semi-structured data." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1199284713.

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Dhifli, Wajdi. "Topological and domain Knowledge-based subgraph mining : application on protein 3D-structures." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2013. http://tel.archives-ouvertes.fr/tel-00946989.

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This thesis is in the intersection of two proliferating research fields, namely data mining and bioinformatics. With the emergence of graph data in the last few years, many efforts have been devoted to mining frequent subgraphs from graph databases. Yet, the number of discovered frequentsubgraphs is usually exponential, mainly because of the combinatorial nature of graphs. Many frequent subgraphs are irrelevant because they are redundant or just useless for the user. Besides, their high number may hinder and even makes further explorations unfeasible. Redundancy in frequent subgraphs is mainly

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Skácel, Jan. "Získávání znalostí z obchodních procesů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-235011.

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This thesis explains business process mining and it's principles. A substantial part is devoted to the problems of process discovery. Further, based on the analysis of specific manufacturing process are proposed three methods that are trying to identify shortcomings in the process. First discovers the manufacturing process and renders it into a graph. The second method uses simulator of production history to obtain products that may caused delays in the process. Acquired data are used to mine frequent itemsets. The third method tries to predict processing time on the selected workplace using a

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Quiroga, Quiroga Oscar Arnulfo. "Discovering frequent and significant episodes. Application to sequences of events recorded in power distribution networks." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/97160.

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This thesis proposes a formalism to analyse and automatically exploit sequences of events, which are related with faults occurred in power distribution networks and are recorded by power quality monitors at substations. This formalism allows to find dependencies or relationships among events, looking for meaningful patterns. Once those patterns are found, they can be used to better describe fault situations and their temporal evolution or can be also useful to predict future failures by recognising the events that match the early stages of a pattern.<br>En aquesta tesi es proposa un formalisme

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Le, Thanh Nam. "Graph representation and mining applied in comic images retrieval." Thesis, La Rochelle, 2019. http://www.theses.fr/2019LAROS008.

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Nous abordons d’abord le problème de la représentation sous forme d’un graphe de l’image et de ses applications en reconnaissance des formes, en mettant l’accent sur les applications de recherche d’images basées sur le contenu (CBIR). Les images utilisées dans cette thèse sont des images de bandes dessinées, qui possèdent des spécificités qui sont des freins pour les méthodes de recherche d’information par le contenu utilisées dans la littérature. Le contenu des bandes dessinées, tel que les objets et les personnages, est complexe. La représentation des personnages, par exemple, peut, d’une ca

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RICUPERO, GIUSEPPE. "Exploring Data Hierarchies to Discover Knowledge in Different Domains." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2744938.

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Gao, Jizhou. "VISUAL SEMANTIC SEGMENTATION AND ITS APPLICATIONS." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/14.

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This dissertation addresses the difficulties of semantic segmentation when dealing with an extensive collection of images and 3D point clouds. Due to the ubiquity of digital cameras that help capture the world around us, as well as the advanced scanning techniques that are able to record 3D replicas of real cities, the sheer amount of visual data available presents many opportunities for both academic research and industrial applications. But the mere quantity of data also poses a tremendous challenge. In particular, the problem of distilling useful information from such a large repository of

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Faci, Adam. "Représentation, simulation et exploitation de connaissances dans le formalisme des graphes conceptuels." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS419.

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Cette thèse aborde le domaine de la représentation des connaissances sous forme de graphes conceptuels, un formalisme de représentation structurée des connaissances proposant des outils de manipulation efficaces. Il existe une multitudes de problématiques en intelligence artificielles symbolique concernant la représentation des connaissances en général, et en particulier sur la représentation concise d’un grand nombre d’informations. Les graphes conceptuels, par la possibilité de représenter visuellement différentes sortes de connaissances, notamment ontologiques et factuelle, et la propositio

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Kutty, Sangeetha. "Enriching XML documents clustering by using concise structure and content." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48326/1/Sangeetha_Kutty_Thesis.pdf.

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With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, thi

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"Fast frequent pattern mining." 2003. http://library.cuhk.edu.hk/record=b5891575.

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Yabo Xu.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.<br>Includes bibliographical references (leaves 57-60).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgement --- p.iii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Frequent Pattern Mining --- p.1<br>Chapter 1.2 --- Biosequence Pattern Mining --- p.2<br>Chapter 1.3 --- Organization of the Thesis --- p.4<br>Chapter 2 --- PP-Mine: Fast Mining Frequent Patterns In-Memory --- p.5<br>Chapter 2.1 --- Background --- p.5<br>Chapter 2.2 --- The Overview --- p.6<br>Chapter 2.3 --- PP-tree Repre

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Carmichael, Christopher Lee. "Visualization for frequent pattern mining." 2013. http://hdl.handle.net/1993/18326.

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Data mining algorithms analyze and mine databases for discovering implicit, previously unknown and potentially useful knowledge. Frequent pattern mining algorithms discover sets of database items that often occur together. Many of the frequent pattern mining algorithms represent the discovered knowledge in the form of a long textual list containing these sets of frequently co-occurring database items. As the amount of discovered knowledge can be large, it may not be easy for most users to examine and understand such a long textual list of knowledge. In my M.Sc. thesis, I represent both the ori

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Tseng, Fan-Chen, and 曾繁鎮. "Mining Frequent Patterns with the Frequent Pattern List." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/45333441171833013337.

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博士<br>國立臺灣大學<br>資訊工程學研究所<br>90<br>The mining of frequent patterns is an essential and time-consuming step in many tasks of data mining. Therefore, algorithms for efficient mining of frequent patterns are in urgent demand. In its original definition, a frequent pattern is a set of items (called itemset) whose occurrence (called support or frequent) in the database exceeds some user-defined threshold. However, the mining of the complete set of frequent itemsets (denoted as FIS) often results in a huge solution space, and the effectiveness of the association rules derived from them will be decr

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Chen, Che-Chuan, and 陳哲專. "Mining Frequent Closed Itemsets with the Frequent Pattern List." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/35016080413390049591.

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Xu, Bei. "Fault-tolerant frequent pattern mining with classifiers." 2006. http://digital.library.okstate.edu/etd/umi-okstate-1927.pdf.

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Ma, Wei-Chien, and 馬維謙. "A Novel Algorithm for Frequent Pattern Mining." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/v98b9c.

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碩士<br>國立高雄應用科技大學<br>資訊工程系<br>102<br>As the advancement of internet technology, millions of data are generated every day. The large amount of data brings new challenge in the discovery of frequent patterns. In this paper, we propose an algorithm that can mine frequent patterns from big data under limited memory. We first utilize hierarchical clustering to partition the data, and make sure the data dependency is kept. Afterwards, the FP-growth is use to mine frequent patterns from each partitioned data. The proposed architecture can also be applied to distributed computing.

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Chung, Zue-Kai, and 張斯凱. "Knowledge Discovery of Mining Frequent Priced Pattern." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/48277031651348005953.

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碩士<br>國立中央大學<br>資訊管理研究所<br>92<br>Association rule mining is very useful for retail business, including of cross sell, customers segment, bundle buying, and so on. Price issue is an important factor in real business environment, but there are few studies which discussed the relation between association rules and price of products. This paper presents an integral framework which can find frequent patterns under price factor. The framework also can provide the sale fact in sale data and analysis the possible suggestion according to the fact. We use real supermarket data on experiment. The actual

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Ai-WeiChuang and 莊璦瑋. "Spatiotemporal Frequent Pattern Mining : A Case Study inCrime Pattern Analysis." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/hq49vv.

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碩士<br>國立成功大學<br>資訊工程學系<br>102<br>Spatiotemporal pattern mining tried to discover unknown, potentially interesting and useful event sequences where events occur within a specific time interval and locate geographic close to each others. Previous works use partition or ill-defined representation of spatial objects and neglect some spatial properties exist in original spatiotemporal data. Moreover, traditional sequential pattern mining methods don't suit the non-transactional spatialtemporal database. In this paper we expose the disappearance of spatial correlation due to improper data representa

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Shang, Xuequn [Verfasser]. "SQL based frequent pattern mining / von: Xuequn Shang." 2005. http://d-nb.info/975449176/34.

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Yang, Che-wei, and 楊哲偉. "Distributed Frequent Pattern Mining Using Time-slice Method." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/57071038759584026194.

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碩士<br>國立中正大學<br>資訊管理所暨醫療資訊管理所<br>97<br>Mining frequent pattern itemsets in databases is popular in the mining for association rules. There were many methods such as Apriori-like or FPgrowth-like algorithms proposed to find frequent patterns. But both of them cannot be applied nowadays because database grows extremely fast that even the FP-tree cannot entirely fit into main memory. Hence, many parallel and distributed algorithms were later proposed to solve such problems. But they require obtaining infrequent patterns by scanning the disk. Hence, the Tidset method based on Parallel FP-tree algo

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Li, Te-Yuan, and 李德原. "frequent pattern mining using mul-level AVLtransaction trees." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/81760979867258633220.

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碩士<br>國立東華大學<br>資訊工程學系<br>91<br>In recent years, data mining research is attracting more and more researchers because of its wide domain of applications including multimedia data analysis/classification, knowledge discovery, …, etc. One of the important issues in data mining is the mining of frequent patterns which provides the bases for association rule mining. There have been a number of methods developed for mining frequent patterns. On designing these mining methods, memory space required for the the transaction database and the execution time required for mining frequent patterns are two

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HUANG, LIN-WEI, and 黃林威. "Multi-Label Learning Based on Frequent Pattern Mining." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2cdqcp.

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Chester, Sean. "Scalable APRIORI-based frequent pattern discovery." Thesis, 2009. http://hdl.handle.net/1828/1370.

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Frequent itemset mining, the task of finding sets of items that frequently occur to- gether in a dataset, has been at the core of the field of data mining for the past sixteen years. In that time, the size of datasets has grown much faster than has the ability of existing algorithms to handle those datasets. Consequentely, improvements are needed. In this thesis, we take the classic algorithm for the problem, A Priori, and improve it quite significantly by introducing what we call a vertical sort. We then use the benchmark large dataset, webdocs, from the FIMI 2004 conference to contr

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