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Relevant bibliographies by topics / Data mining; the rules of association; apriori algorithm / Dissertations / Theses

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Dissertations / Theses on the topic 'Data mining; the rules of association; apriori algorithm'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Kilinc, Yasemin. "Mining Association Rules For Quality Related Data In An Electronics Company." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610459/index.pdf.

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Quality has become a central concern as it has been observed that reducing defects will lower the cost of production. Hence, companies generate and store vast amounts of quality related data. Analysis of this data is critical in order to understand the quality problems and their causes, and to take preventive actions. In this thesis, we propose a methodology for this analysis based on one of the data mining techniques, association rules. The methodology is applied for quality related data of an electronics company. Apriori algorithm used in this application generates an excessively large numbe

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2

Thomas, Wessel Morant. "Parallel Mining of Association Rules Using a Lattice Based Approach." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/361.

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The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. Parallel algorithms are required for the mining of association rules due to the very large databases used to store the transactions. In this paper we present a parallel algorithm for the mining of association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorith

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3

Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.

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4

Toprak, Serkan. "Data Mining For Rule Discovery In Relational Databases." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605356/index.pdf.

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Data is mostly stored in relational databases today. However, most data mining algorithms are not capable of working on data stored in relational databases directly. Instead they require a preprocessing step for transforming relational data into algorithm specified form. Moreover, several data mining algorithms provide solutions for single relations only. Therefore, valuable hidden knowledge involving multiple relations remains undiscovered. In this thesis, an implementation is developed for discovering multi-relational association rules in relational databases. The implementation is based on

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5

Aloquio, Lyvia. "Análise associativa: identificação de padrões de associação entre o perfil socioeconômico dos alunos do ensino básico e os resultados nas provas de matemática." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=6724.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Nos dias atuais, a maioria das operações feitas por empresas e organizações é armazenada em bancos de dados que podem ser explorados por pesquisadores com o objetivo de se obter informações úteis para auxílio da tomada de decisão. Devido ao grande volume envolvido, a extração e análise dos dados não é uma tarefa simples. O processo geral de conversão de dados brutos em informações úteis chama-se Descoberta de Conhecimento em Bancos de Dados (KDD - Knowledge Discovery in Databases). Uma das etapas deste processo é a Mineração de Dad

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6

Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).

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7

Ferreira, José Alves. "Data mining em banco de dados de eletrocardiograma." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/98/98131/tde-15072014-094917/.

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Neste estudo, foi proposta a exploração de um banco de dados, com informações de exames de eletrocardiogramas (ECG), utilizado pelo sistema denominado Tele-ECG do Instituto Dante Pazzanese de Cardiologia, aplicando a técnica de data mining (mineração de dados) para encontrar padrões que colaborem, no futuro, para a aquisição de conhecimento na análise de eletrocardiograma. A metodologia proposta permite que, com a utilização de data mining, investiguem-se dados à procura de padrões sem a utilização do traçado do ECG. Três pacotes de software (Weka, Orange e R-Project) do tipo open source foram

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8

Abdo, Walid A. A. "Enhancing association rules algorithms for mining distributed databases. Integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5661.

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Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distrib

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9

Abdo, Walid Adly Atteya. "Enhancing association rules algorithms for mining distributed databases : integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5661.

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Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distrib

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10

Savulionienė, Loreta. "Association rules search in large data bases." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140519_102242-45613.

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The impact of information technology is an integral part of modern life. Any activity is related to information and data accumulation and storage, therefore, quick analysis of information is necessary. Today, the traditional data processing and data reports are no longer sufficient. The need of generating new information and knowledge from given data is understandable; therefore, new facts and knowledge, which allow us to forecast customer behaviour or financial transactions, diagnose diseases, etc., can be generated applying data mining techniques. The doctoral dissertation analyses modern da

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11

Li, Yanrong. "Techniques for improving clustering and association rules mining from very large transactional databases." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/907.

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Clustering and association rules mining are two core data mining tasks that have been actively studied by data mining community for nearly two decades. Though many clustering and association rules mining algorithms have been developed, no algorithm is better than others on all aspects, such as accuracy, efficiency, scalability, adaptability and memory usage. While more efficient and effective algorithms need to be developed for handling the large-scale and complex stored datasets, emerging applications where data takes the form of streams pose new challenges for the data mining community. The

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12

Ambraziūnas, Valdas. "Didelių duomenų sekų analizės problemos." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2004. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2004~D_20040611_164618-10612.

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The main goal of these thesis is to compare association rules finding algorithms and to indicate the usability of finding association rules in business area. In order to achieve this goal, the theoretical analysis of three algorithms is done: 1. The Apriori algorithm – the most well known association rule algorithm – based on the property: “Any subset of a large itemset must be large”. This algorithm assumes that the database is memory-resident. The maximum number of database scans is one more than the cardinality of the largest large itemset. 2. The Sampling algorithm deals with the database

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13

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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14

Vlk, Vladimír. "Získávání znalostí z webových logů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236196.

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This master's thesis deals with creating of an application, goal of which is to perform data preprocessing of web logs and finding association rules in them. The first part deals with the concept of Web mining. The second part is devoted to Web usage mining and notions related to it. The third part deals with design of the application. The forth section is devoted to describing the implementation of the application. The last section deals with experimentation with the application and results interpretation.

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Bodeček, Miroslav. "Algoritmus pro cílené doporučování produktů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-412860.

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The goal of this project is to explore the problem of product recommendations in the area of e-commerce and to evaluate known techniques, design product recommendation system for an existing e-commerce site, implement it and test it. This report introduces the problem, briefly examines current state of affairs in this area and defines requirements for a product recommendation module. The concept of data mining in general is introduced. The report proceeds to present detailed design corresponding to defined requirements and summarizes data gathered during testing phase. It concludes with evalua

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16

Savulionienė, Loreta. "Susietumo taisyklių paieška didelėse duomenų bazėse." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140519_102254-19589.

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Informacinių technologijų įtaka neatsiejama nuo šiuolaikinio gyvenimo. Bet kokia veiklos sritis yra susijusi su informacijos, duomenų kaupimu, saugojimu. Šiandien nebepakanka tradicinio duomenų apdorojimo bei įvairių ataskaitų formavimo. Duomenų tyrybos technologijų taikymas leidžia iš turimų duomenų išgauti naujus faktus ar žinias, kurios leidžia prognozuoti veiklą, pavyzdžiui, pirkėjų elgesį ar finansines tendencijas, diagnozuoti ligas ir pan. Disertacijoje nagrinėjami duomenų tyrybos algoritmai dažniems posekiams ir susietumo taisyklėms nustatyti. Disertacijoje sukurtas naujas stochastinis

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17

Pumprla, Ondřej. "Získávání znalostí z datových skladů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236715.

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This Master's thesis deals with the principles of the data mining process, especially with the mining of association rules. The theoretical apparatus of general description and principles of the data warehouse creation is set. On the basis of this theoretical knowledge, the application for the association rules mining is implemented. The application requires the data in the transactional form or the multidimensional data organized in the Star schema. The implemented algorithms for finding of the frequent patterns are Apriori and FP-tree. The system allows the variant setting of parameters fo

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18

Casanova, Anderson Araújo. "MINERAÇÃO DE DADOS: ALGORITMO DA CONFIANÇA INVERSA." Universidade Federal do Maranhão, 2005. http://tedebc.ufma.br:8080/jspui/handle/tede/373.

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Made available in DSpace on 2016-08-17T14:52:55Z (GMT). No. of bitstreams: 1 Anderson Araujo Casanova.pdf: 587331 bytes, checksum: 45bf9a1dbbcfa2f595d1baf7e3651125 (MD5) Previous issue date: 2005-06-28<br>This work presents studies that culminated in the development of a data mining algorithm that extracts knowledge in a more efficient way and allows for a better use of the collected information. Decisions based on imprecise information and a lack of criteria can cause the relatively few resources available to be poorly applied, burdening taxpayers and consequently the state. This much-nee

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19

Oliveira, Joana Raquel Carias de. "Data Mining na procura de nova informação: Market Basket Analysis aplicado a um dataset público." Master's thesis, 2019. http://hdl.handle.net/10400.26/37552.

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Hoje em dia, a população encontra-se sobrecarregada com dados, quando todas as atividades realizadas pelas organizações e pessoas, no seu dia-a-dia, geram dados. Contudo, o facto de termos acesso a um enorme volume de dados não significa que tenhamos acesso a muita informação ou conhecimento. É, portanto, importante trabalhar os dados por forma a gerar informação relevante para a tomada de decisão, pois num mundo globalizado e extremamente competitivo, um minuto pode ser fulcral para fechar um negócio e, para tal, é necessário ter acesso à informação atual, correta e sumarizada. Face ao volum

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20

VARSHNEY, PEEYUSH. "CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16143.

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Data mining process are followed an extensive undertaking of research and product improvement. This development started when enterprise material was first loaded on computers, continued with advancement in data access, and more recently, developed technologies that permit users to transport through their data in real time. APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. In this report, I study the data structure

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VARSHNEY, PEEYUSH. "CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16318.

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Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. In this report, I examine the data structure, implement

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22

Ansari, Sohaib Zafar. "Market basket analysis : trend analysis of association rules in different time periods." Master's thesis, 2019. http://hdl.handle.net/10362/80955.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRM<br>Market basket analysis (i.e. Data mining technique in the field of marketing) is the method to find the associations between the items / item sets and based on those associations we can analyze the consumer behavior. In this research we have presented the variability of time, because with the change in time the habits or behavior of the customer also changes. For example, people wear warm clothes in winter and light cloth

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23

HSU, TZU-YUN, and 許紫畇. "A High Efficient Apriori Algorithm of Mining Association Rules." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/46032077254468294962.

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24

Yao, Chiao Yin, and 姚喬尹. "Improving the Efficiency of the Apriori Algorithm for Mining Association Rules." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/65311017636604670428.

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碩士<br>南台科技大學<br>資訊管理系<br>98<br>With the development of information technology, enterprises have a lot of way to get information and can use this technology store about a lot of enterprise’s transaction or record in data base. How to find the useful information in database has become the subject which the enterprises pay attention. Association rules technology is generally in data mining. Based on the Internet Technology development and the globalization of business, the transaction database of enterprise is constantly changing all the time, and in order to keep the accuracy of exploring result

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Pinheiro, Fabiola M. R. "Applying the Apriori and FP-Growth Association Algorithms to Liver Cancer Data." Thesis, 2013. http://hdl.handle.net/1828/4846.

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Cancer is the leading cause of deaths globally. Although liver cancer ranks only fourth in incidence worldwide among all types of cancer, its survivability rate is the lowest. Liver cancer is often diagnosed at an advanced stage, because in the early stages of the disease patients usually do not have signs or symptoms. After initial diagnosis, therapeutic options are limited and tend to be effective only for small size tumors with limited spread and minimal vascular invasion. As a result, long-term patient survival remains minimal, and has not improved in the past three decades. In order

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26

CHOU, WAN-JEN, and 周琬禎. "Utilizing Fuzzy Theory and Apriori Algorithm to mine Association Rules in Numerical Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fuqj5z.

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碩士<br>國立雲林科技大學<br>工業工程與管理系<br>106<br>In the recently years, technology and the Internet are quickly developed. It makes many kinds of data increasing rapidly. There is a lot of data which are helpful to the decision maker, hiding in the database. In order to get the useful information from the data for the reference of the decision, the technique called Data Mining was developed. Among them, Association Rules is one of the widely used. There are many kinds of Association Rule algorithms. Apriori algorithm is the earliest and most representative one. Apriori algorithm tries to find out the larg

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27

Kabir, MMJ. "New evolutionary algorithms for mining interesting association rules." Thesis, 2016. https://eprints.utas.edu.au/23454/1/Kabir_whole_thesis.pdf.

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This PHD thesis deals with the evolutionary algorithms for mining frequent patterns and discovering useful and interesting Boolean association rules from large data sets. Initially, the classical algorithms for mining frequent patterns and single and multi- objective evolutionary algorithms for discovering association rules using different measures are studied. Secondly, the problem of extracting frequent patterns using classical algorithms and obtaining a set of high quality association rules relying on the evolutionary algorithms are addressed. The objectives of this thesis are as follows:

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28

Chen, Hung-lung, and 陳宏隆. "Run length encoding-- based algorithm for mining association rules in data stream." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/90489529147629035129.

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碩士<br>南華大學<br>資訊管理學研究所<br>96<br>It is a new developing research field that the materials bunch flows and prospects, and the RLEated rule performs algorithms and is prospected by the materials (Data Mining) A quite important and practical technology in China. The RLEated type rule main purpose is to find out the dependence of some materials projects in the huge materials. The main method is to search the database and find out all high-frequency project teams; And utilize the high-frequency project team to excavate out all RLEated type rules.Because of the production of a large number of materia

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29

Chang, Wei-Hao, and 張韋豪. "An Efficient Algorithm for Mining Frequent Closed Itemsets and Non-Redundant Association Rules in Data Streams." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/47thcp.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>96<br>It can analyze customer’s behaviors who bought products from transaction databases by association rules mining. Traditional algorithms for association rules mining can mine the frequent itemsets over static transactional databases. When we insert a new transaction or delete a transaction, we have to remine the entire updated database. It’s costly in both time and space requirement. Data comes with high speed, unbound, and continuous. So some scholars propose an idea to find all frequent itemsets and association rules in data streams. However, the amount of freq

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NEHA. "CUSTOMER RETENTION ANALYSIS." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14920.

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Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. In our research work we have used FP-Tree based approach for mining single-level frequent patterns. We proposed a novel frequent-pattern tree (FP-tree) structure, which is an extended prefixtree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Methodology for Mi

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31

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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