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Dissertations / Theses on the topic 'A Frequent'

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Published: 5 June 2025

Last updated: 2 August 2025

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1

Alnatsheh, Rami H. "Frequent Itemset Hiding Algorithm Using Frequent Pattern Tree Approach." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/76.

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A problem that has been the focus of much recent research in privacy preserving data-mining is the frequent itemset hiding (FIH) problem. Identifying itemsets that appear together frequently in customer transactions is a common task in association rule mining. Organizations that share data with business partners may consider some of the frequent itemsets sensitive and aim to hide such sensitive itemsets by removing items from certain transactions. Since such modifications adversely affect the utility of the database for data mining applications, the goal is to remove as few items as possible.

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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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Soztutar, Enis. "Mining Frequent Semantic Event Patterns." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611007/index.pdf.

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Especially with the wide use of dynamic page generation, and richer user interaction in Web, traditional web usage mining methods, which are based on the pageview concept are of limited usability. For overcoming the difficulty of capturing usage behaviour, we define the concept of semantic events. Conceptually, events are higher level actions of a user in a web site, that are technically independent of pageviews. Events are modelled as objects in the domain of the web site, with associated properties. A sample event from a video web site is the &#039<br>play video event&#039<br>with properties

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Eom, Boyun. "Query optimization using frequent itemset mining." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0010844.

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Wang, Liang, and 王亮. "Frequent itemsets mining on uncertain databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B4590215X.

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Schlegel, Benjamin. "Frequent itemset mining on multiprocessor systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-141763.

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Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent years, there have been many frequent-itemset mining algorithms proposed, which however (1) often have high memory requirements and (2) do not exploit the large degrees of parallelism provided by modern mul

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PULVIRENTI, FABIO. "Frequent Itemset Mining for Big Data." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2696539.

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Traditional data mining tools, developed to extract actionable knowledge from data, demonstrated to be inadequate to process the huge amount of data produced nowadays. Even the most popular algorithms related to Frequent Itemset Mining, an exploratory data analysis technique used to discover frequent items co-occurrences in a transactional dataset, are inefficient with larger and more complex data. As a consequence, many parallel algorithms have been developed, based on modern frameworks able to leverage distributed computation in commodity clusters of machines (e.g., Apache Hadoop, Apache

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Amiri, Mohsen. "Cervical musculoskeletal impairment in frequent intermittent headache /." [St. Lucia, Qld.], 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18168.pdf.

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

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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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Stoecker-Sylvia, Zachary. "Mining for frequent events in time series." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0902104-163011/.

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14

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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Jiang, Chuntao. "Frequent subgraph mining algorithms on weighted graphs." Thesis, University of Liverpool, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569788.

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This thesis describes research work undertaken in the field of graph-based knowledge discovery (or graph mining). The objective of the research is to investigate the benefits that the concept of weighted frequent subgraph mining can offer in the context of the graph model based classification. Weighted subgraphs are graphs where some of the vertexes/edges are considered to be more significant than others. How to discover frequent sub-structures with different strengths is the main issue to be resolved in this thesis. The main approach to addressing this issue is to integrate weight constraints

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

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Aridhi, Sabeur. "Distributed frequent subgraph mining in the cloud." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2013. http://tel.archives-ouvertes.fr/tel-00951350.

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Recently, graph mining approaches have become very popular, especially in certain domains such as bioinformatics, chemoinformatics and social networks. One of the most challenging tasks in this setting is frequent subgraph discovery. This task has been highly motivated by the tremendously increasing size of existing graph databases. Due to this fact, there is urgent need of efficient and scaling approaches for frequent subgraph discovery especially with the high availability of cloud computing environments. This thesis deals with distributed frequent subgraph mining in the cloud. First, we pro

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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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Neal, Richard David. "Patterns of frequent attendance to general practice." Thesis, University of Leeds, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440361.

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Hammar, Charlotte. "The ambiguous and frequent task of revision." Thesis, Malmö högskola, Lärarutbildningen (LUT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-34064.

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Mitt syfte med denna uppsats har varit att ta reda på om läroböckerna i religionskunskap, vid namn, Sofi – Religion och Religionskunskap – Kompakt är lämpliga och aktuella att använda i undervisning trots att kursplanen kommer att förändras höstterminen 2011. Min undersökning har främst varit inriktad på kunskapssynen i kursplanen för religionskunskap, ur den nya läroplanen för grundskolan, och i de ovan nämnda läroböckerna. För att kunna identifiera kunskapssynen i läroböckerna har jag studerat de instuderingsuppgifter som finns i respektive lärobok. Dessa har jag sedan kategoriserat och förs

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Durmaz, Arda. "FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING NETWORKS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149704489838836.

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Mishra, Satyakam. "Frequent Subgraph Mining Analysis of GPCR Activation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1613575702373053.

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23

He, Ruofei. "Bayesian mixture models for frequent itemset mining." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/bayesian-mixture-models-for-frequent-itemset-mining(6d88d0d1-3066-4545-8565-56d651eeadc4).html.

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In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely used in the development of probability models in machine learning in recent years and these methods have many advantages, including their abilities to avoid overfitting. In this thesis, we develop two Bayesian mixture models with the Dirichlet distribution prior and the Dirichlet p

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Sen, Tayfun. "Parallel Closet+ Algorithm For Finding Frequent Closed Itemsets." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610742/index.pdf.

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Data mining is proving itself to be a very important field as the data available is increasing exponentially, thanks to first computerization and now internetization. On the other hand, cluster computing systems made up of commodity hardware are becoming widespread, along with the multicore processor architectures. This high computing power is synthesized with data mining to process huge amounts of data and to reach information and knowledge. Frequent itemset mining is a special subtopic of data mining because it is an integral part of many types of data mining tasks. Often this task is a pr

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Stickney, Remington Bigelow, and Remington Bigelow Stickney. "Transitional Care of Elderly Frequent Emergency Department Users." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/626348.

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Background: Frequent ED users are generally over the age of 65 years, Medicare beneficiaries, sicker and have more health issues than non-frequent users. Elderly patients suffer a 20% mortality rate upon admission and a 30% decrease in activities of daily living (ADL) after discharge. Transitional care programs (TCP) decrease ED visits and readmission rates, improves ADLs, and increases event-free survival. Purpose: To evaluate the need of an ED TCP in the ED. Aims are to assess ED providers’, nurses’ and managers’ perceptions of elderly frequent ED users’ discharge needs, resources, and pote

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Welke, Pascal [Verfasser]. "Efficient Frequent Subtree Mining Beyond Forests / Pascal Welke." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/1188732161/34.

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Khan, Khalid Salim. "Healthcare Access among Adults with Frequent Mental Distress." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/608267.

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A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.<br>Objective: Mental health plays a central role in the well‐being of individuals. Understanding the factors that influence mental wellness is critical in order to develop effective policy that addresses the burden of mental illness in society. The objective of this study is to identify a possible relationship between healthcare access and the presence of mental distress in individuals. Methods: Logistic regression was performed usi

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

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

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

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Wang, Yun-Ru, and 王韻茹. "Mining Frequent Subspaces." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/37625542053261184907.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>96<br>As both the number of dimensions and the amount of data increase, existing clustering methods in the full feature space are not good enough to cluster the data in databases. Thus, the subspace clustering has attracted more and more attention recently. In this thesis, we proposed a novel subspace mining method which can simultaneously consider all frequent subspaces to select the significant subspaces. The proposed method consists of three phases. First, we project all data points onto each pair of dimensions and generate frequent subspaces. Second, we join fre

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Cheng, Ching-Wei, and 鄭景瑋. "Frequent Subspace Classifier." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/65886346622257718847.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>97<br>With the amount of the data increasing rapidly, it is infeasible to consider all the dimensions of the data to perform classification. Thus, constructing a classifier based on subspaces has attracted more and more attention. The previously proposed methods used randomly-generated or some subspaces to construct a classifier. Therefore, in this thesis, we propose a hybrid classification method, called FSC (Frequent subspace classifier), to generate all potential subspaces and utilize these subspaces to construct a classifier. Our proposed method consists of thre

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Wang, Yun-Ru. "Mining Frequent Subspaces." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2907200814274000.

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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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Chiu, Chui-huang, and 邱垂煌. "Mining Discriminative Frequent Patterns." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/92292055003596097985.

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碩士<br>世新大學<br>資訊管理學研究所(含碩專班)<br>98<br>The rapid development of information technology enables companies to accumulate a large amount of data in short time. Many enterprises nowadays pursue finding ways to utilize data and convert them to information or knowledge. As for retailing sales, huge amount of operational data is accumulated everyday, therefore these sales records are good sources to analyse by kinds of data mining technology to discover useful information. In this study, our goal is to mine discriminative patterns in transaction databases. A discriminative pattern is defined as a co

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Abdelhamid, Ehab. "Scalable Frequent Subgraph Mining." Diss., 2017. http://hdl.handle.net/10754/625049.

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A graph is a data structure that contains a set of nodes and a set of edges connecting these nodes. Nodes represent objects while edges model relationships among these objects. Graphs are used in various domains due to their ability to model complex relations among several objects. Given an input graph, the Frequent Subgraph Mining (FSM) task finds all subgraphs with frequencies exceeding a given threshold. FSM is crucial for graph analysis, and it is an essential building block in a variety of applications, such as graph clustering and indexing. FSM is computationally expensive, and its exis

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Chen, Yi-An, and 陳怡安. "Mining Frequent Trajectory Patterns." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/29743026116836899524.

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博士<br>國立臺灣大學<br>資訊管理學研究所<br>99<br>In this dissertation, we propose three algorithms, GBM, FTM and LTM, for mining trajectory patterns. GBM focuses on finding frequent trajectory patterns consisting of consecutively adjacent points, where the time spent between two consecutive points in a frequent trajectory pattern is represented by a timespan. FTM mines frequent flexible trajectory patterns, where the consecutive points in a flexible pattern are not necessarily adjacent and the time spent between two consecutive points is denoted by a time interval. Although representing a trajectory pattern

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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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Liu, Yu-mei, and 劉佑玫. "Mining of Frequent Subgraph Patterns." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/2mfm6y.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>100<br>In recent years, data mining has been extensively applied to different domains. How to find frequent patterns efficiently in large data sets is a popular research topic in the data mining community. In addition, the power of using graphs to model complex data sets has been recognized based on the researches in the past. Therefore, using graphs to represent data and developing the data mining technique has turned into a main trend. The purpose of graph mining is to find frequent subgraphs from graph data sets. In other words, it is to discover all the structure

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Li, Cheng-Jhe, and 李承哲. "Mining of Frequent Embedded Unordered Subtrees." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/30316068815480403208.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>104<br>Tree mining in data mining field has been a very popular research issue. This is because it can be applied to many kinds of tree-represented documents such as web logs, XMLdocuments, XBRL(eXtensible Business Reporting Language) documents for financial statements, and other semi-structured documents, etc. However, how to efficiently find all frequent subtrees from a tree database has always been the main concern of many scholars. So the main challenges are how to avoid duplicate enumerated subtrees and identify potential frequent candidate subtrees in order to

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Yu, Tsui-Fen, and 余翠芬. "Incremental Mining of Frequent Subgraph Patterns." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/56099182798144564817.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>93<br>How to find frequent patterns efficiently in large data sets is an active research topic in the data mining community. In recent years, mining frequent patterns has been extensively applied to different domains. In the past, mining frequent patterns was focused on itemsets and path patterns. Nevertheless, with the increasing domains applied by the data mining technique and more complex data sets in new domains, the traditional mining technique of the frequent patterns will be not enough. Therefore, it has turned into a main trend to develop an efficient and sui

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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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Chiu, Chien-Yi, and 邱建益. "Frequent Pattern based Continuous Account Identification." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/2425wz.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>102<br>Cloud Computing is a mature technology that attracts people’s attention and is considered as the main part of the network and computing service provider in recent years. Some security issues will be more threatening in cloud computing, such as account theft and insider threat. We propose a framework to utilize anomaly detection and random re- sampling techniques for profiling a user’s behaviors via the frequent patterns of activated system processes. By utilizing the user profiles learned from normal data, our method can detect malicious activities and discrim

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Chiang, Li-Hsiang, and 姜禮翔. "Frequent Pattern Hiding in Incremental Database." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/22248530127159529662.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>98<br>In recent year, researchers are starting to focus on the issue of privacy. Many issues are starting to focus how to protect the sensitive information of dataset owner under data mining technology too. The frequent pattern hiding is also an important issue in privacy preserving data mining technology. However, in the explosion of information and rapid development of the internet the data which stored in database is usually continually add and update. On the other hand, the exits algorithms are designed addressed to the static database such that the exits algori

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Yeh, Chun-Ting, and 葉俊廷. "Using Prediction To Mine Frequent Itemsets." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/26956067169404480013.

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Su, Ya-Wen, and 蘇雅雯. "Frequent Subgraph Mining with Timing Constraints." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/15497642967335948867.

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蘇仁鑫. "Mining frequent patterns by transaction tree." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/79464139456448545300.

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Huang, Ko-Wei, and 黃科瑋. "Mining Frequent Patterns with Heterogeneous Constraints." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/61039732706261007215.

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碩士<br>國立高雄大學<br>電機工程學系碩士班<br>96<br>Recently, the topic of constraint-based association mining has received increasing attention within the data mining research community. By allowing more user-specified constraints other than traditional rule measurements, e.g., minimum support and confidence, research work on this topic endeavor to reflect real interest of analysts and relief them from the overabundance of rules, and ultimately, fulfill an interactive environment for association analysis. So far most work on constraint-based frequent patterns (itemsets) mining has been single-constraint orien

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Schlegel, Benjamin. "Frequent itemset mining on multiprocessor systems." Doctoral thesis, 2013. https://tud.qucosa.de/id/qucosa%3A27984.

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Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent years, there have been many frequent-itemset mining algorithms proposed, which however (1) often have high memory requirements and (2) do not exploit the large degrees of parallelism provided by modern mul

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Yang, Bei-Chuan, and 楊倍權. "Mining Frequent Itemsets from Uncertain Database." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22190984293586456359.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>101<br>Mining frequent itemsets from the transaction database is order to find often purchased the combination of products. In other words, contain this itemset transactions reached a user-defined threshold and the transaction record of the transaction purchase those product items. We can combine frequent itemset for promotional merchandise to achieve the purpose of increasing sales. Mining frequent itemsets currently most efficient way is to use FP-Tree structure and the transaction database only records whether the product was purchased. However, in some applicati

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Nien, Chia-Chang, and 粘嘉菖. "Mining Frequent Episodes on Data Streams." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03264312408289986161.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>97<br>Data mining is a useful technique for data analysis, One of kind is mining frequent episodes in sequences. Users can predict event by mining frequent episodes in the future. They use level-wise in traditional methods, for example, candidate episodes are generated firstly, and we will find the frequent episodes by mining sequences. They spend a lot of time on scanning sequences and searching candidate episodes. In addition, data add with time in sequences continuously, it is called data streams. They consume time in scanning sequence. Data streams can be use in

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