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Relevant bibliographies by topics / Data Mining; Frequent pattern Mining; Utility Mining / Dissertations / Theses

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Dissertations / Theses on the topic 'Data Mining; Frequent pattern Mining; Utility Mining'

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Published: 3 June 2025

Last updated: 31 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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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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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. "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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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PrasadJaysawal, Bijay, and 畢杰. "Frequent Sequential Pattern and High Utility Pattern Mining in the Data Stream Environments." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4c4kvn.

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博士<br>國立成功大學<br>電腦與通信工程研究所<br>107<br>In this dissertation, we addressed frequent sequential pattern and high utility pattern mining in data streams environment. In data stream mining, it is desirable that the algorithms perform the operations using single-pass. Moreover, algorithms are required to be efficient in terms of execution time. In addition, it is also desirable that the algorithms require less amount of memory usage. We first explored frequent sequential pattern mining in multiple streams. Specifically, we proposed an efficient algorithm named PSP-AMS to progressively mine frequent

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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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Chun-WeiLin and 林浚瑋. "Extension of Frequent Pattern Trees for Incremental, Utility and Fuzzy Itemsets Mining." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/12518159241175818181.

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博士<br>國立成功大學<br>資訊工程學系碩博士班<br>98<br>Data mining, also referred to as knowledge discovery, has recently emerged as an important research topic, and association rules mining is considered as one of the most referenced sub-topics in data mining. In the past, traditional algorithms process all records in a batch way for mining association rules. In real-world applications, records are constantly being inserted, deleted or modified in dynamic databases. Designing an algorithm that can efficiently maintain association rules in dynamic databases is critically important. In the first part of this dis

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Tu, Yun-Sheng, and 凃耘昇. "In Pursuit of Efficient Frequent Pattern Mining in Data Streams." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/69910263528048154910.

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碩士<br>淡江大學<br>電機工程學系碩士班<br>104<br>Data stream mining is a research topic in the big data field. Data streams contain a lot of data which are generated continuously in our daily life. They are click data, sensor data, transaction data, network traffic data and so on. There are a lot of valuable information in data streams. Data mining techniques have been used to analyze data in different applications so that making better decisions. In data mining fields, frequent pattern mining is an important topic. It is used to find patterns which appeared frequently. Frequent patterns are used in several

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Khumalo, Siyabonga Nicholus, and Siyabonga Nicholus Khumalo. "Study for Frequent Pattern Mining on Continuous Uncertain Data Streams." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/qqr974.

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碩士<br>國立臺北科技大學<br>電資學院外國學生專班<br>103<br>Mining of frequent patterns in uncertain databases is one of the popular knowledge discovery and data mining task. Frequent pattern mining is simply a method of finding interesting patterns like item sets, subsequences and substructures that repeatedly occur in datasets. In uncertain databases the uncertainty is caused by lack of assurance in existence or non-existence of objects and can also be caused by the presence of errors in databases. Due to error in data sets, poor mining methods or techniques can lead to wrong relationships and patterns in freque

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Wen, Bo-Wei, and 溫柏為. "A Rapid Incremental Frequent Pattern Mining Algorithm for Uncertain Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8dj742.

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碩士<br>國立嘉義大學<br>資訊管理學系研究所<br>106<br>Association rule analysis is an important topic in data mining. Basket analysis is one of the most well-known application. Stores or retailers can get better sales through the analysis of goods combination. For example, placing beer and diapers at the same place can bring greater sales for the stores. However, due to the rapid increase in the amount of data in this big data era, how to mine frequent patterns from big data has become an important issue. Many approaches were proposed to solve the incremental problem of certain data, but these approaches did no

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Ibrahim, A. "Effective Characterization of Sequence Data through Frequent Episodes." Thesis, 2015. http://etd.iisc.ac.in/handle/2005/3969.

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Pattern discovery is an important area of data mining referring to a class of techniques designed for the extraction of interesting patterns from the data. A pattern is some kind of a local structure that captures correlations and dependencies present in the elements of the data. In general, pattern discovery is about finding all patterns of `interest' in the data and a popular measure of interestingness for a pattern is its frequency of occurrence in the data. Thus the problem of frequent pattern discovery is to find all patterns in the data whose frequency of occurrence exceeds some user def

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Ibrahim, A. "Effective Characterization of Sequence Data through Frequent Episodes." Thesis, 2015. http://etd.iisc.ernet.in/2005/3969.

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Pattern discovery is an important area of data mining referring to a class of techniques designed for the extraction of interesting patterns from the data. A pattern is some kind of a local structure that captures correlations and dependencies present in the elements of the data. In general, pattern discovery is about finding all patterns of `interest' in the data and a popular measure of interestingness for a pattern is its frequency of occurrence in the data. Thus the problem of frequent pattern discovery is to find all patterns in the data whose frequency of occurrence exceeds some user def

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CAMPAGNI, RENZA. "Data Mining Models for Student Databases." Doctoral thesis, 2013. http://hdl.handle.net/2158/803882.

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This thesis presents a data mining methodology to analyze the careers of University students, where a career is the ordered sequence of the exams taken by the single student. We present different models based on clustering, classification and sequential pattern techniques in order to understand and improve the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using

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Vimieiro, Renato. "Mining disjunctive patterns in biomedical data sets." Thesis, 2012. http://hdl.handle.net/1959.13/936341.

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Research Doctorate - Doctor of Philosophy (PhD)<br>Frequent itemset mining is one of the most studied problems in data mining. Since Agrawal et al. (1993) introduced the problem, several advances both theoretical and practical have been achieved. In spite of that, there are still many unresolved issues to be tackled before frequent pattern mining can be claimed a cornerstone approach in data mining (Han et al., 2007). Here, we investigate issues related to: (1) the (un)suitability of frequent itemset mining algorithms to identify patterns in biomedical data sets; and (2) the limited expressive

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Chung, Sheng-Hao, and 鍾勝好. "A Novel Frequent Pattern Mining Algorithm for Big Data under Limited Memory." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/19432931829247493539.

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碩士<br>國立高雄應用科技大學<br>資訊工程系<br>101<br>Data mining aims to mine the hidden useful information from transaction databases. For example, a company can apply data mining to the discovery of association rule from transaction databases to improve the commercial services by understanding the customer behavior. However, the data size increases with the time, which incurs the scalability problem of mining. In this thesis, we propose a method that is able to mine the frequent patterns for big data under limited memory. Through empirical evaluation on various simulation conditions, our proposed method is s

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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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Wu, Tz-ke, and 吳子科. "A Novel and Efficient Distributed Data Mining Algorithm Based on Frequent Pattern-Tree." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/14377152327481581455.

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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>In this paper, we proposed a novel algorithm which is implemented on the distributed system that can efficiently solve the problem of FP-tree. The algorithm uses divide and conquer to split big database into several sub-database. With data division, the algorithm can reduce the total execution time of algorithm. The algorithm doesn’t construct the whole FP-tree. Instead, with intermittence, a time division mechanism, the algorithm can also efficiently reduce the execution time. We also compress the data into a one dimensional array while transmitting, whi

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"A study of frequent pattern and association rule mining: with applications in inventory update and marketing." 2004. http://library.cuhk.edu.hk/record=b5891887.

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Wong, Chi-Wing.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.<br>Includes bibliographical references (leaves 149-153).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgement --- p.iv<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- MPIS --- p.3<br>Chapter 1.2 --- ISM --- p.5<br>Chapter 1.3 --- MPIS and ISM --- p.5<br>Chapter 1.4 --- Thesis Organization --- p.6<br>Chapter 2 --- MPIS --- p.7<br>Chapter 2.1 --- Introduction --- p.7<br>Chapter 2.2 --- Related Work --- p.10<br>Chapter 2.2.1 --- Item Selection Related Work --- p.11<br>Chapter 2.3

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GOEL, VIVEK. "EFFICIENT ALGORITHM FOR FREQUENT PATTERN MINING AND IT’S APPLICATION IN PREDICTING PATTERN IN WEB USAGE DATA." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13930.

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M.TECH<br>Frequent Pattern Mining, the task of finding sets of items that frequently occur together in a dataset, has been at the core of the field of data mining for the past many years. With the tremendous growth of data, users are expecting more relevant and sophisticated information which may be lying hidden in the data. Data mining is often described as a discipline to find hidden information in a database. It involves different techniques and algorithms to discover useful knowledge lying hidden in the data. In this thesis, we propose an efficient algorithm for finding the frequent patt

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Abouelnagah, Younes. "Efficient Temporal Synopsis of Social Media Streams." Thesis, 2013. http://hdl.handle.net/10012/7689.

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Search and summarization of streaming social media, such as Twitter, requires the ongoing analysis of large volumes of data with dynamically changing characteristics. Tweets are short and repetitious -- lacking context and structure -- making it difficult to generate a coherent synopsis of events within a given time period. Although some established algorithms for frequent itemset analysis might provide an efficient foundation for synopsis generation, the unmodified application of standard methods produces a complex mass of rules, dominated by common language constructs and many trivial variat

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Huang, Chao-Ying, and 黃昭頴. "Applying Frequent-Pattern Trees in Data Mining to Evaluate Users'' Physical Fitness and Mental Health Conditions." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/yd74g3.

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碩士<br>國立臺北科技大學<br>電機工程系研究所<br>100<br>Due to the pressure from daily life, there is an increase in depression population. The diagnosis and treatment of depression is indispensible for patients. The severity of depression is an important reference factor before psychiatric outpatient service. But the traditional questionnaire is answered by pen on the papers. Then, the responded questionnaire is analyzed by professional assistants. In this way, we could not determine the depression degree immediately. This study is aimed on designing a physical fitness and mental health evaluation system that i

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Laxman, Srivatsan. "Discovering Frequent Episodes : Fast Algorithms, Connections With HMMs And Generalizations." Thesis, 2006. https://etd.iisc.ac.in/handle/2005/375.

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Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come up naturally in a wide range of application domains, ranging from bioinformatics to manufacturing processes. Pattern discovery refers to a broad class of data mining techniques in which the objective is to unearth hidden patterns or unexpected trends in the data. In general, pattern discovery is about finding all patterns of 'interest' in the data and one popular measure of i

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Laxman, Srivatsan. "Discovering Frequent Episodes : Fast Algorithms, Connections With HMMs And Generalizations." Thesis, 2006. http://hdl.handle.net/2005/375.

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Abstract:

Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come up naturally in a wide range of application domains, ranging from bioinformatics to manufacturing processes. Pattern discovery refers to a broad class of data mining techniques in which the objective is to unearth hidden patterns or unexpected trends in the data. In general, pattern discovery is about finding all patterns of 'interest' in the data and one popular measure of i

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Liu, Chunyang. "Summarizing data with representative patterns." Thesis, 2016. http://hdl.handle.net/10453/52923.

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University of Technology Sydney. Faculty of Engineering and Information Technology.<br>The advance of technology makes data acquisition and storage become unprecedentedly convenient. It contributes to the rapid growth of not only the volume but also the veracity and variety of data in recent years, which poses new challenges to the data mining area. For example, uncertain data mining emerges due to its capability to model the inherent veracity of data; spatial data mining attracts much research attention as the widespread of location-based services and wearable devices. As a fundamental topic

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Wang, Yu-Kai, and 王昱凱. "The Application of Data Mining Technique on Frequent Prescription Pattern Analysis among Different-Level Hospitals :Taking Diabetes mellitus and Essential Hypertension as Examples." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/61557238901516782870.

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碩士<br>南台科技大學<br>企業管理系<br>93<br>The Department of Health has divided Taiwan hospitals into Medicare Center, Regional Hospital, Local Hospital, and Primary Medicare Care four levels; it aggressively pushes the “Hierarchical Medical Care System,” to expect the levels of hospitals can guard people’s health with the reasonable division of labor; although it also attempts to implement “the Hierarchical Medical Care System” by pricing factor; nevertheless, due to the defects of system design, the tendency of people to go for better hospital services and to take more medicines, so the general public n

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