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1

Jagannath, Sandhya. "Utility Guided Pattern Mining." NCSU, 2003. http://www.lib.ncsu.edu/theses/available/etd-11262003-131929/.

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This work is an initial exploration of the use of the decision-theoretic concept of utility to guide pattern mining. We present the use of utility functions as against thresholds and constraints as the mechanism to express user preferences and formulate several pattern mining problems that use utility functions. Utility guided pattern mining provides the twin benefits of capturing user preferences precisely using utility functions and of expressing user focus by choosing an appropriate utility guided pattern mining problem. It addresses the drawbacks of threshold guided pattern mining, the spe
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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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Coussat, Aurélien. "Pattern Mining in Uncertain Tensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265629.

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Data mining is the art of extracting information from data and creating useful knowledge. Itemset mining, or pattern mining, is an important subfield that consists in finding relevant patterns in datasets. We focus on two subproblems: high-utility itemset mining, where a numerical value called utility is associated to every tuple of the dataset, and patterns are extracted whose utilities sum up to a high-enough value; and skypattern mining, which is the extraction of patterns optimizing various measures, using the notion of Pareto domination. To tackle both of these challenges, we follow a gen
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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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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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Lattner, Andreas D. "Temporal pattern mining in dynamic environments /." Berlin : Akad. Verl.-Ges. Aka, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2995598&prov=M&dok_var=1&dok_ext=htm.

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Termier, Alexandre. "Pattern mining rock: more, faster, better." Habilitation à diriger des recherches, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-01006195.

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Le pattern mining est un domaine du data mining dont le but est l'extraction de régularité dans les données. Ce document présente nos contributions au domaine selon 3 axes : 1. Le domaine du pattern mining est jeune et il y existe encore beaucoup de types de régularités qu'un analyste serait intéressé de découvrir mais qui ne sont pas encore gérées. Nous avons contribué à deux nouveaux types de patterns: les patterns graduels et les patterns périodiques avec "ruptures". Nous avons aussi proposé ParaMiner, un algorithme original pour le pattern mining générique, qui permet à des analystes de sp
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Mordvanyuk, Natalia. "Efficient sequential and temporal pattern mining." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/672924.

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The contributions of the present thesis are in the domain of Pattern Mining and Knowledge Discovery, being of particular relevance for the sequential pattern mining and time-interval related pattern mining fields. In this thesis, a new efficient sequential pattern mining algorithm called VEPRECO is introduced, the contributions of which are: (i) a new representation, (ii) pre-pruning strategies and (iii) candidate selection policies which reduce the number of iterations of the algorithm. In this thesis, a new efficient algorithm for mining time interval patterns, called vertTIRP, has als
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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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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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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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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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Spyropoluou, Eirini. "Local pattern mining in multi-relational data." Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654116.

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Multi-relational data mining has so far been synonym to methods based on Inductive Logic Programming (ILP) , which discover frequent first-order logic rules in the data. This is due to the fact that ILP conveniently captures the multi-relational structure, while there has not been a suitable pattern syntax extension of an itemset for the case of multi-relational data. Local pattern mining methods have mostly focused on mining a single relation. A common strategy for mining multi-relational data (MRD) has been to apply frequent items et mining on the join of all database relations. However, whe
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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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Baumgarten, Matthias. "Multi-dimensional sequential and associative pattern mining." Thesis, University of Ulster, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412149.

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Orakzai, Faisal Moeen. "Movement Pattern Mining over Large-Scale Datasets." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/285611/4/TOC.pdf.

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Movement pattern mining involves the processing of movement data to understand the mobility behaviour of humans/animals. Movement pattern mining has numerous applications, e.g. traffic optimization, event planning, optimization of public transport and carpooling. The recent digital revolution has caused a wide-spread use of smartphones and other devices equipped with GPS. These devices produce a tremendous amount of movement data which contains valuable mobility information. Many interesting mobility patterns and algorithms to mine them have been proposed in recent years to mine different type
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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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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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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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Yu, Ping. "FP-tree Based Spatial Co-location Pattern Mining." Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4724/.

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A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investi
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Onal, Kezban Dilek. "A New Wap-tree Based Sequential Pattern Mining Algorithm For Faster Pattern Extraction." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614638/index.pdf.

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Sequential pattern mining constitutes a basis for solution of problems in various domains like bio-informatics and web usage mining. Research on this field continues seeking faster algorithms. WAP-Tree based algorithms that emerged from web usage mining literature have shown a remarkable performance on single-item sequence databases. In this study, we investigated application of WAP-Tree based mining to multi-item sequential pattern mining and we designed an extension of WAP-Tree data structure for multi-item sequence databases, the MULTI-WAP-Tree. In addition, we propose a new mining strategy
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Narducci, Nicola. "Mining di pattern rilevati da dati di traiettoria." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14193/.

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Negli ultimi anni si è potuto assistere ad una grandissima diffusione di smartphone dotati di sensori sempre più precisi ed accurati. In particolare il sensore GPS (Global Positioning System) all’interno di questi dispositivi ha permesso di generare una quantità sempre maggiore di informazioni legate alla posizione degli smartphone (e quindi delle persone). L’elaborazione di questi dati spazio-temporali trova spazio all’interno del progetto di ricerca “Modelling social behaviours from trajectory”. In questo scenario il lavoro proposto di seguito si concentra sull’estrapolazione di pattern per
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Morita, Tomoyuki, Yasushi Hirano, Yasuyuki Sumi, Shoji Kajita, and Kenji Mase. "A Pattern Mining Method for Interpretation of Interaction." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10366.

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Otaki, Keisuke. "Algorithmic Approaches to Pattern Mining from Structured Data." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/215673.

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The contents of Chapter 6 are based on work published in IPSJ Transactions on Mathematical Modeling and Its Applications, vol.9(1), pp.32-42, 2016.<br>Kyoto University (京都大学)<br>0048<br>新制・課程博士<br>博士(情報学)<br>甲第19846号<br>情博第597号<br>新制||情||104(附属図書館)<br>32882<br>京都大学大学院情報学研究科知能情報学専攻<br>(主査)教授 山本 章博, 教授 鹿島 久嗣, 教授 阿久津 達也<br>学位規則第4条第1項該当
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Wu, Jianfei. "Vector-Item Pattern Mining Algorithms and their Applications." Diss., North Dakota State University, 2011. https://hdl.handle.net/10365/28841.

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Advances in storage technology have long been driving the need for new data mining techniques. Not only are typical data sets becoming larger, but the diversity of available attributes is increasing in many problem domains. In biological applications for example, a single protein may have associated sequence-, text-, graph-, continuous and item data. Correspondingly, there is growing need for techniques to find patterns in such complex data. Many techniques exist for mapping specific types of data to vector space representations, such as the bag-of-words model for text [58] or embedding in vec
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Qormosh, Bassam M. M. "Classifying Gene Coexpression Networks Using Discrimination Pattern Mining." Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/28009.

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Several algorithms for graph classi cation have been proposed. Algorithms that map graphs into feature vectors encoding the presence/absence of speci c subgraphs, have shown excellent performance. Most of the existing algorithms mine for subgraphs that appear frequently in graphs belonging to one class label and not so frequently in the other graphs. Gene coexpression networks classi cation attracted a lot of attention in the recent years from researchers in both biology and data mining because of its numerous useful applications. The advances in high-throughput technologies that provide
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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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Shie, Bai-En, and 謝百恩. "Mining Sequential Patterns with Pattern Constraints." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/4yw437.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>94<br>Sequential pattern mining is to find sequential behaviors which most customers frequently do in a transaction database. These behaviors are called sequential patterns. There were many papers proposed algorithms for finding all sequential patterns. However, there is a new problem: users may only need some special sequential patterns, for example, the sequential patterns which include certain items or behaviors. If we let users set the items or patterns which they are interested in before mining process, we will save much execution time and the sequential pat
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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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"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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Sá, Cláudio Frederico Pinho Rebelo de. "Pattern Mining for Label Ranking." Doctoral thesis, 2016. https://hdl.handle.net/10216/111033.

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Lin, Hsuan-Yu, and 林宣佑. "Image Pattern Mining and Counting." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8z62aq.

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碩士<br>國立臺灣大學<br>工程科學及海洋工程學研究所<br>105<br>Object counting is common and important in life. Currently there are many research about counting objects in an image, but most of them need to adjust parameters base on target profiles. It leads to some limits on data type which make these approaches inflexible. In this paper, we take repeat and obvious contours as pattern, describing their profiles and counting numbers without setting data conditions in advance. In this paper, the definition of pattern is the contours with similar features on shape, color and size simultaneously.We extracts contour fea
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Sá, Cláudio Frederico Pinho Rebelo de. "Pattern Mining for Label Ranking." Tese, 2016. https://repositorio-aberto.up.pt/handle/10216/111033.

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Lin, Ming-Yen, and 林明言. "Efficient Algorithms for Association Rule Mining and Sequential Pattern Mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/m8z62p.

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博士<br>國立交通大學<br>資訊工程系所<br>92<br>Tremendous amount of data being collected is increasing speedily by computerized applications around the world. Hidden in the vast data, the valuable information is attracting researchers of multiple disciplines to study effective approaches to derive useful knowledge from within. Among various data mining objectives, the mining of frequent patterns has been the focus of knowledge discovery in databases. This thesis aims to investigate efficient algorithms for mining frequent patterns including association rules and sequential patterns. We propose the LexMiner a
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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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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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Waranashiwar, Shruti Dilip. "Interactive pattern mining of neuroscience data." Thesis, 2014. http://hdl.handle.net/1805/3878.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Text mining is a process of extraction of knowledge from unstructured text documents. We have huge volumes of text documents in digital form. It is impossible to manually extract knowledge from these vast texts. Hence, text mining is used to find useful information from text through the identification and exploration of interesting patterns. The objective of this thesis in text mining area is to find compact but high quality frequent patterns from text documents related to neuroscience field. We try to prove that interactive sampling
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Hu, Hsing-Yuan, and 胡星垣. "Multi-domain Simultaneous Sequential Pattern Mining." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/sp79zk.

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碩士<br>國立交通大學<br>資訊工程系所<br>94<br>Sequential pattern mining has attracted a significant amount of research efforts recently. The problem of sequential pattern mining is that discovering frequent sequences with their occurrence counts being larger than or equal to the user-specified number, min_support, among a set of sequences. Most of the previously sequential pattern mining methods only explore mining sequential patterns in one domain, such as buy behavior, Web browsing, and moving patterns. In reality, sequential patterns may exist in multiple sequence databases and for these sequential patte
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Wei, Ling-Yin, and 魏綾音. "Trajectory Pattern Mining in Social Media." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/94683412264379902062.

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博士<br>國立交通大學<br>資訊科學與工程研究所<br>100<br>The increasing availability of location-acquisition technology, such as GPS, leads to various mobile social applications, and thus people can share their locations, photos, and activities in socialWebs. For example, people can record and share their trips on the Web by GPS tracks or people can perform check-in services to share their visited locations. In fact, the user-generated spatio-temporal data could be viewed as trajectories. In this dissertation, we study how to explore patterns and propose algorithms from trajectories for pattern-aware trip plannin
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Ting, Chia-Hsien, and 丁嘉賢. "Progressive Sequential Interval-based Pattern Mining." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/01778210542325079878.

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碩士<br>靜宜大學<br>資訊管理學系研究所<br>98<br>Abstract Generally speaking, the sequential pattern mining usually utilizes the transaction ID, item number, customer number, and the occurrence time to discover useful patterns. Among these records in the database, the sequential pattern mining usually only employs the starting points to mine frequent patterns, but ignores the ending points. This result may fail to obtain some useful patterns. In the addition of practical applications, mining in the static database and incremental database has already can not satisfied with up-to-date demand. This may has no s
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Lai, Guan-Lin, and 賴冠霖. "Workflow Pattern mining and Its applications." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/42905275672377619217.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>98<br>Process oriented ERP system will store the activities records in the event logs. In general, there exist certain interrelationships among these activities called “business activities rules”. However, it would be difficult for manager to identify these embedded business rules from the event logs. Therefore, if a mining approach can be developed to discover these rules, it can be used as reference information in the management level function such as auditing. In this research, an approach based on genetic process mining algorithm to discover the business rules wi
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Tsai, Chi Chang, and 蔡吉章. "Pattern Mining on English Grammatical Relations." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/72249234732234082770.

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碩士<br>國立政治大學<br>資訊科學學系<br>97<br>Some study found some common ESL (English as a Second Language) learners English Writing Error: improper use of the word, the verb form is not correct, the sentence lack of subject and verb tense errors. These errors are mainly due to: lack of vocabulary, grammar concept is not clear, the mother-tongue interference. In order to improve the ESL writing, we hope that the information from the grammatical relation to provide assistance. At present, the studies of grammatical relation mostly emphasize the word consisting of a single grammatical relation. However, wor
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Chen, Yen-Wen, and 陳彥文. "An Efficient Sequential Pattern Mining System." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/71105279822216796304.

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碩士<br>淡江大學<br>資訊管理學系<br>92<br>To speed up the process of sequential pattern mining, in this thesis, three effective methods are proposed and are used as the basis to implement an Efficient Sequential Pattern Mining system(ESPM). The features of the three methods are described as follow: first, a modified vertical TID-lists are designed to lower the times of matching during mining. Secondly, we found that the length of each transaction, thus the matching time, can be further reduced by removing the items with low supports. Besides, similar to [2], the hash table is also used to improve the eff
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Ferreira, Pedro Gabriel Dias. "Sequence pattern mining in biochemical data." Doctoral thesis, 2007. http://hdl.handle.net/1822/7257.

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Tese de Doutoramento em Informática na Especialidade de Inteligência Artificial<br>O recente aumento exponencial do número de sequências genéticas acessíveis através de bases de dados especializadas na internet apresenta grandes decanos para a comunidade científica. Um desses decanos consiste na pesquisa eficiente e efectiva de padrões sequenciais, também chamados motins, entre um conjunto de sequencias de proteínas relacionadas. Tais padrões descrevem regiões que podem fornecer importantes indicações sobre a estrutura e funcionalidade das proteínas analisadas. Considerando os actuais av
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Huang, Nancy, and 黃安婷. "Discriminative Pattern Mining in Microbiomic Data." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/29121629577865058185.

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博士<br>國立臺灣大學<br>資訊工程學研究所<br>104<br>Machine learning classifiers have long been used to solve biological problems by predicting the target class (e.g. disease state, bacterial taxonomy, etc.) of unseen samples. A favorable and important byproduct of a special type of classifier is “interpretability” (also known as “comprehensibility”), which could be utilized to offer explanations as to why and how a sample is assigned to the predicted class. Interpretable classifiers produce “discriminative patterns” that lead to different prediction results, and provide insights to critical properties of the
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