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Dissertations / Theses on the topic 'Mining Techniques and Pattern Discovery'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Skabar, Andrew Alojz. "Inductive learning techniques for mineral potential mapping." Thesis, Queensland University of Technology, 2001.

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2

Cao, Huiping. "Pattern discovery from spatiotemporal data." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37381520.

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Cao, Huiping, and 曹會萍. "Pattern discovery from spatiotemporal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37381520.

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4

Pipanmaekaporn, Luepol. "A data mining framework for relevance feature discovery." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62857/1/Luepol_Pipanmaekaporn_Thesis.pdf.

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This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

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Ke, Yiping. "Efficient correlated pattern discovery in databases /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20KE.

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6

Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16675/1/Sheng-Tang_Wu_Thesis.pdf.

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In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single w

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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16675/.

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In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single w

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8

Preti, Giulia. "On the discovery of relevant structures in dynamic and heterogeneous data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242978.

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We are witnessing an explosion of available data coming from a huge amount of sources and domains, which is leading to the creation of datasets larger and larger, as well as richer and richer. Understanding, processing, and extracting useful information from those datasets requires specialized algorithms that take into consideration both the dynamism and the heterogeneity of the data they contain. Although several pattern mining techniques have been proposed in the literature, most of them fall short in providing interesting structures when the data can be interpreted differently from user t

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Preti, Giulia. "On the discovery of relevant structures in dynamic and heterogeneous data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242978.

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We are witnessing an explosion of available data coming from a huge amount of sources and domains, which is leading to the creation of datasets larger and larger, as well as richer and richer. Understanding, processing, and extracting useful information from those datasets requires specialized algorithms that take into consideration both the dynamism and the heterogeneity of the data they contain. Although several pattern mining techniques have been proposed in the literature, most of them fall short in providing interesting structures when the data can be interpreted differently from user to

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ZANONI, MARCO. "Data mining techniques for design pattern detection." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/31515.

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The main objective of design pattern detection is to gain better comprehension of a software system, and of the kind of problems addressed during the development of the system itself. Design patterns have informal specifications, leading to many implementation variants caused by the subjective interpretation of the pattern by developers. This thesis applies a supervised classification approach to make the detection more subjective, bringing to developers the patterns they want to find, ranked by a confidence value.

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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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12

Algarni, Abdulmohsen. "Relevance feature discovery for text analysis." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48230/1/Abdulmohsen_Algarni_Thesis.pdf.

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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a pro

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13

Kohlsdorf, Daniel. "Data mining in large audio collections of dolphin signals." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53968.

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The study of dolphin cognition involves intensive research of animal vocal- izations recorded in the field. In this dissertation I address the automated analysis of audible dolphin communication. I propose a system called the signal imager that automatically discovers patterns in dolphin signals. These patterns are invariant to frequency shifts and time warping transformations. The discovery algorithm is based on feature learning and unsupervised time series segmentation using hidden Markov models. Researchers can inspect the patterns visually and interactively run com- parative statistics bet

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14

Chen, Xi. "Using data mining techniques to discover customer behavioral patterns for direct marketing in mobile telecommunication industry." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40987942.

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Howard, Craig M. "Tools and techniques for knowledge discovery." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368357.

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Chen, Xi, and 陳熹. "Using data mining techniques to discover customer behavioral patterns for direct marketing in mobile telecommunication industry." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40987942.

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17

Atzmüller, Martin. "Knowledge-intensive subgroup mining : techniques for automatic and interactive discovery /." Berlin : Aka, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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18

Nabhan, Ahmed Ragab. "Graph Pattern Mining Techniques to Identify Potential Model Organisms." ScholarWorks @ UVM, 2014. http://scholarworks.uvm.edu/graddis/4.

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Recent advances in high throughput technologies have led to an increasing amount of rich and diverse biological data and related literature. Model organisms are classically selected as subjects for studying human disease based on their genotypic and phenotypic features. A significant problem with model organism identification is the determination of characteristic features related to biological processes that can provide insights into the mechanisms underlying diseases. These insights could have a positive impact on the diagnosis and management of diseases and the development of therapeutic dr

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Atzmüller, Martin. "Knowledge-intensive subgroup mining techniques for automatic and interactive discovery." Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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Kanodia, Juveria. "Structural advances for pattern discovery in multi-relational databases /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/978.

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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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Gawande, Rashmi. "Evaluation of Automotive Data mining and Pattern Recognition Techniques for Bug Analysis." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-196770.

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In an automotive infotainment system, while analyzing bug reports, developers have to spend significant time on reading log messages and trying to locate anomalous behavior before identifying its root cause. The log messages need to be viewed in a Traceviewer tool to read in a human readable form and have to be extracted to text files by applying manual filters in order to further analyze the behavior. There is a need to evaluate machine learning/data mining methods which could potentially assist in error analysis. One such method could be learning patterns for “normal” messages. “Normal” coul

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Mooney, Carl Howard, and carl mooney@bigpond com. "The Discovery of Interacting Episodes and Temporal Rule Determination in Sequential Pattern Mining." Flinders University. Informatics and Engineering, 2007. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20070702.120306.

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The reason for data mining is to generate rules that can be used as the basis for making decisions. One such area is sequence mining which, in terms of transactional datasets, can be stated as the discovery of inter-transaction associations or associations between different transactions. The data used for sequence mining is not limited to data stored in overtly temporal or longitudinally maintained datasets and in such domains data can be viewed as a series of events, or episodes, occurring at specific times. The problem thus becomes a search for collections of events that occur frequently tog

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ZHU, YAOYAO. "UNSUPERVISED DATABASE DISCOVERY BASED ON ARTIFICIAL INTELLIGENCE TECHNIQUES." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1024314290.

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Di, Silvestro Lorenzo Paolo. "Data Mining and Visual Analytics Techniques." Doctoral thesis, Università di Catania, 2014. http://hdl.handle.net/10761/1559.

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With the beginning of the Information Age and the following spread of the information overload phenomenon, it has been mandatory to develop a means to simply explore, analyze and summarize large quantity of data. To achieve this purposes a data mining techniques and information visualization methods are used since decades. In the last years a new research field is gaining importance: Visual Analytics, an outgrowth of the fields of scientific and information visualization but includes technologies from many other fields, including knowledge management, statistical analysis, cognitive science an

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Li, Hsin-Fang. "DATA MINING AND PATTERN DISCOVERY USING EXPLORATORY AND VISUALIZATION METHODS FOR LARGE MULTIDIMENSIONAL DATASETS." UKnowledge, 2013. http://uknowledge.uky.edu/epb_etds/4.

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Oral health problems have been a major public health concern profoundly affecting people’s general health and quality of life. Given that oral health data is composed of several measurable dimensions including clinical measurements, socio-behavioral factors, genetic predispositions, self-reported assessments, and quality of life measures, strategies for analyzing multidimensional data are neither computationally straightforward nor efficient. Researchers face major challenges to identify tools that circumvent the processes of manually probing the data. The purpose of this dissertation is to pr

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27

Chen, Xiaodong. "Temporal data mining : algorithms, language and system for temporal association rules." Thesis, Manchester Metropolitan University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297977.

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Studies on data mining are being pursued in many different research areas, such as Machine Learning, Statistics, and Databases. The work presented in this thesis is based on the database perspective of data mining. The main focuses are on the temporal aspects of data mining problems, especially association rule discovery, and issues on the integration of data mining and database systems. Firstly, a theoretical framework for temporal data mining is proposed in this thesis. Within this framework, not only potential patterns but also temporal features associated with the patterns are expected to

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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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Chen, Jonathan Jun Feng. "Data Mining/Machine Learning Techniques for Drug Discovery: Computational and Experimental Pipeline Development." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524661027035591.

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30

Scott, Daniel. "The discovery of new functional oxides using combinatorial techniques and advanced data mining algorithms." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/15214/.

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Electroceramic materials research is a wide ranging field driven by device applications. For many years, the demand for new materials was addressed largely through serial processing and analysis of samples often similar in composition to those already characterised. The Functional Oxide Discovery project (FOXD) is a combinatorial materials discovery project combining high-throughput synthesis and characterisation with advanced data mining to develop novel materials. Dielectric ceramics are of interest for use in telecommunications equipment; oxygen ion conductors are examined for use in fuel c

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Snyder, Ashley M. (Ashley Marie). "Data mining and visualization : real time predictions and pattern discovery in hospital emergency rooms and immigration data." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61199.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 163-166).<br>Data mining is a versatile and expanding field of study. We show the applications and uses of a variety of techniques in two very different realms: Emergency department (ED) length of stay prediction and visual analytics. For the ED, we investigate three data mining techniques to predict a patient's length of stay based solely on the information available at the patient's arrival. We

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Iglesia, Beatriz de la. "The development and application of heuristic techniques for the data mining task of nugget discovery." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368386.

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Minnen, David. "Unsupervised discovery of activity primitives from multivariate sensor data." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24623.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Thad Starner; Committee Member: Aaron Bobick; Committee Member: Bernt Schiele; Committee Member: Charles Isbell; Committee Member: Irfan Essa

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Lu, Jing. "From sequential patterns to concurrent branch patterns : a new post sequential patterns mining approach." Thesis, University of Bedfordshire, 2006. http://hdl.handle.net/10547/556399.

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Sequential patterns mining is an important pattern discovery technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been intensively studied and there exists a great diversity of algorithms. However, there is a major problem associated with the conventional sequential patterns mining in that patterns derived are often large and not very easy to understand or use. In addition, more complex relations among events are often hidden behind sequences. A novel model for sequential patterns called Sequential Patterns Graph (SPG) is p

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Welch, SJ. "Interactive visualisation techniques for data mining of satellite imagery." Thesis, Honours thesis, University of Tasmania, 2006. https://eprints.utas.edu.au/933/1/front_matter_welch.pdf.

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Supervised classification of satellite imagery largely removes the user from the information extraction process. Visualisation is an often ignored means by which users may interactively explore the complex patterns and relationships in satellite imagery. Classification can be considered a 'hypothesis testing' form of analysis. Visual Data Mining allows for dynamic hypothesis generation, testing and revision based on a human user's perception. In this study Visual Data Mining was applied to the classification of satellite imagery. After reviewing appropriate techniques and literature a tool wa

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Fu, Xuezheng. "Structure Pattern Analysis Using Term Rewriting and Clustering Algorithm." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/17.

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Biological data is accumulated at a fast pace. However, raw data are generally difficult to understand and not useful unless we unlock the information hidden in the data. Knowledge/information can be extracted as the patterns or features buried within the data. Thus data mining, aims at uncovering underlying rules, relationships, and patterns in data, has emerged as one of the most exciting fields in computational science. In this dissertation, we develop efficient approaches to the structure pattern analysis of RNA and protein three dimensional structures. The major techniques used in this wo

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Bijaksana, Moch Arif. "Decision boundary setting and classifier combination for text classification." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/82827/1/Moch%20Arif_Bijaksana_Thesis.pdf.

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This thesis presents a promising boundary setting method for solving challenging issues in text classification to produce an effective text classifier. A classifier must identify boundary between classes optimally. However, after the features are selected, the boundary is still unclear with regard to mixed positive and negative documents. A classifier combination method to boost effectiveness of the classification model is also presented. The experiments carried out in the study demonstrate that the proposed classifier is promising.

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Carkacioglu, Levent. "Automated Biological Data Acquisition And Integration Using Machine Learning Techniques." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610396/index.pdf.

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Since the initial genome sequencing projects along with the recent advances on technology, molecular biology and large scale transcriptome analysis result in data accumulation at a large scale. These data have been provided in different platforms and come from different laboratories therefore, there is a need for compilation and comprehensive analysis. In this thesis, we addressed the automatization of biological data acquisition and integration from these non-uniform data using machine learning techniques. We focused on two different mining studies in the scope of this thesis. In the first st

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Baron, Steffan. "Temporale Aspekte entdeckten Wissens." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2004. http://dx.doi.org/10.18452/15136.

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In den letzten Jahren haben Anzahl und Umfang verfuegbarer Datensaetze stark zugenommen, wodurch die Entwicklung von Methoden zur Entdeckung von Wissens in den Daten zu einer grossen Herausforderung geworden ist. Waehrend dabei sonst Effizienzfragen im Vordergrund standen, wurde in juengerer Zeit auch die temporale Dimension der Daten einbezogen. Es wurden Methoden erarbeitet, die der Pflege des entdeckten Wissens dienen. Diesen Techniken liegt die Idee zugrunde, dass Daten oft ueber einen langen Zeitraum gesammelt werden. Damit sind sie den gleichen Aenderungen ausgesetzt wie die Realitaet. A

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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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Bhaskaran, Subhashini Sailesh. "An Investigation into the Knowledge Discovery and Data Mining (KDDM) process to generate course taking pattern characterised by contextual factors of students in Higher Education Institution (HEI)." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15880.

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The Knowledge Discovery and Data Mining (KDDM), a growing field of study argued to be very useful in discovering knowledge hidden in large datasets are slowly finding application in Higher Educational Institutions (HEIs). While literature shows that KDDM processes enable discovery of knowledge useful to improve performance of organisations, limitations surrounding them contradict this argument. While extending the usefulness of KDDM processes to support HEIs, challenges were encountered like the discovery of course taking patterns in educational datasets associated with contextual information.

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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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Psorakis, Ioannis. "Probabilistic inference in ecological networks : graph discovery, community detection and modelling dynamic sociality." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:84741d8b-31ea-4eee-ae44-a0b7b5491700.

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This thesis proposes a collection of analytical and computational methods for inferring an underlying social structure of a given population, observed only via timestamped occurrences of its members across a range of locations. It shows that such data streams have a modular and temporally-focused structure, neither fully ordered nor completely random, with individuals appearing in "gathering events". By exploiting such structure, the thesis proposes an appropriate mapping of those spatio-temporal data streams to a social network, based on the co-occurrences of agents across gathering events, w

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Nilsson, Felix. "Joint Human-Machine Exploration of Industrial Time Series Using the Matrix Profile." Thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44717.

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Technological advancements and widespread adaptation of new technology in industry have made industrial time series data more available than ever before. This trend is expected to continue, especially with the introduction of Industry 4.0, where the goal is to connect everything on the industry floor to the cloud and the Industrial Internet of Things. With this development grows the need for versatile methods for mining industrial time series data. Time series motif discovery is a sub-set of data mining and is about finding interesting patterns in time series data. The state of the art in time

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Cavadenti, Olivier. "Contribution de la découverte de motifs à l’analyse de collections de traces unitaires." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI084/document.

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Dans le contexte manufacturier, un ensemble de produits sont acheminés entre différents sites avant d’être vendus à des clients finaux. Chaque site possède différentes fonctions : création, stockage, mise en vente, etc. Les données de traçabilités décrivent de manière riche (temps, position, type d’action,…) les événements de création, acheminement, décoration, etc. des produits. Cependant, de nombreuses anomalies peuvent survenir, comme le détournement de produits ou la contrefaçon d’articles par exemple. La découverte des contextes dans lesquels surviennent ces anomalies est un objectif cent

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Stríž, Rostislav. "Dolování periodických vzorů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236473.

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Data collecting and analysis are commonly used techniques in many sectors of today's business and science. Process called Knowledge Discovery in Databases presents itself as a great tool to find new and interesting information that can be used in a future developement. This thesis deals with basic principles of data mining and temporal data mining as well as with specifics of concrete implementation of chosen algorithms for mining periodic patterns in time series. These algorithms have been developed in a form of managed plug-ins for Microsoft Analysis Services -- service that provides data mi

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Dlamini, Wisdom Mdumiseni Dabulizwe. "Spatial analysis of invasive alien plant distribution patterns and processes using Bayesian network-based data mining techniques." Thesis, 2016. http://hdl.handle.net/10500/20692.

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Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumption

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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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Yamaguchi, Fabian. "Pattern-Based Vulnerability Discovery." Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0023-9682-0.

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

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Abstract:

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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