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Relevant bibliographies by topics / Apriori algorithm

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Academic literature on the topic 'Apriori algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Apriori algorithm"

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Tirumalasetty, Sudhir, A. Aruna, A. Padmini, D. Vijaya Sagaru, and A. Tejeswini. "An Enhanced Apriori with Interestingness of Patterns using cSupport and rSupport." International Journal of Computer Science and Mobile Computing 10, no. 7 (2021): 20–27. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.003.

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Data mining is wide spreading its applications in several areas. There are different tasks in mining which provides solutions for wide variety of problems in order to discover knowledge. Among those tasks association mining plays a pivotal role for identifying frequent patterns. Among the available association mining algorithms Apriori algorithm is one of the most prevalent and dominant algorithm which is used to discover frequent patterns. An enhancement to Apriori algorithm is done i.e. Apriori2 which minimized the number of scans. In this research Apriori2 is modified by including rSupport

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Liu, Xiyu, Yuzhen Zhao, and Minghe Sun. "An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors." Discrete Dynamics in Nature and Society 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6978146.

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Apriori algorithm, as a typical frequent itemsets mining method, can help researchers and practitioners discover implicit associations from large amounts of data. In this work, a fast Apriori algorithm, called ECTPPI-Apriori, for processing large datasets, is proposed, which is based on an evolution-communication tissue-like P system with promoters and inhibitors. The structure of the ECTPPI-Apriori algorithm is tissue-like and the evolution rules of the algorithm are object rewriting rules. The time complexity of ECTPPI-Apriori is substantially improved from that of the conventional Apriori a

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Syahrir, Moch, and Lalu Zazuli Azhar Mardedi. "Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems." MATRIX : Jurnal Manajemen Teknologi dan Informatika 13, no. 2 (2023): 52–67. http://dx.doi.org/10.31940/matrix.v13i2.52-67.

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The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm.

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Karthik, Somu, and Velu C.M. "A Novel Prediction of Sales and Purchase Forecasting for Festival Season of Hypermarkets with Customer Dataset Using Apriori Algorithm Instead of FP-Growth Algorithm to Improve the Accuracy." ECS Transactions 107, no. 1 (2022): 12647–59. http://dx.doi.org/10.1149/10701.12647ecst.

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Aim: To predict the novel and to forecast sales for festival season hypermarkets. Materials and Methods: A total of 484 samples were collected from market datasets available in kaggle. For this two algorithms were used, one is the FP-Growth algorithm and another is Apriori algorithm. Both the algorithms were executed and compared for accuracy. Result: Apriori achieved accuracy, precision, sensitivity and specificity of 73 %,75%, 78%,and 80%, respectively, compared to 71%, 73%, 76%, 75%, and 78% by FP-Growth algorithm, 87.4%, 88.2%, 89.2%, and 93%, respectively, compared to 80.1%, 83.39%, 84%,

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Zazuli, Lalu Zazuli Azhar Mardedi, Kartarina Kartarina, and Moch Syahrir Syahrir. "Analisis Perbandingan Algoritma Fp-Growth Dan Tpq-Apriori Dalam Menentukan Rule Based Terbaik Untuk Sistem Rekomendasi Produk." Explore 14, no. 2 (2024): 55–66. http://dx.doi.org/10.35200/ex.v14i2.112.

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The popular association rule algorithms are a priori and fp-growth, these two algorithms are very familiar among data mining researchers, however there are several weaknesses found in the association rule algorithm, including scanning the dataset for a long time in the process of searching for itemset frequencies, the use of large memory and the resulting base rules are sometimes less than optimal. In this research, the author compared the fp-growth and TPQ-apriori algorithms to analyze the base rule results of the two algorithms. TPQ-Apriori is an algorithm resulting from the development of t

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KANWAL, ATTIYA, SAHAR FAZAL, SOHAIL ASGHAR, and Muhammad Naeem. "SUBGROUP DISCOVERY OF THE MODY GENES;." Professional Medical Journal 20, no. 05 (2013): 644–52. http://dx.doi.org/10.29309/tpmj/2013.20.05.1207.

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Background: The pandemic of metabolic disorders is accelerating in the urbanized world posing huge burden to healthand economy. The key pioneer to most of the metabolic disorders is Diabetes Mellitus. A newly discovered form of diabetes is MaturityOnset Diabetes of the Young (MODY). MODY is a monogenic form of diabetes. It is inherited as autosomal dominant disorder. Till to date11 different MODY genes have been reported. Objective: This study aims to discover subgroups from the biological text documentsrelated to these genes in public domain database. Data Source: The data set was obtained fr

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He, Yue Shun, and Jun Fang Xiao. "Improved Methods on Association Rules Mining Algorithms." Key Engineering Materials 460-461 (January 2011): 148–52. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.148.

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Among the many mining algorithms of association rules, Apriori Algorithm is a classical algorithm that has caused the most discussion; it can effectively carry out the mining association rules. However, based on Apriori Algorithm, most of the traditional algorithms exist "item sets generation bottleneck" problem, and are very time-consuming. An enhanced algorithm associating Apriori with transaction reduction and item reduction technique is put forward by the paper, in the algorithm candidate item sets generation and the support calculation are created after each transaction is compressed and

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Bagga, S., and N. Badal. "D-Apriori: An Algorithm to Incorporate Dynamism in Apriori Algorithm." International Journal of Computer Applications 89, no. 10 (2014): 24–28. http://dx.doi.org/10.5120/15669-4231.

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Zakur, Yahya, and Laith Flaih. "Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review." E3S Web of Conferences 448 (2023): 02021. http://dx.doi.org/10.1051/e3sconf/202344802021.

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Data mining has the potential to empower healthcare organizations by allowing them to analyze various aspects of patient information and discover connections between seemingly unrelated data. By harnessing advanced data analysis techniques, healthcare providers can identify trends in patients' medical conditions and behaviours. The Apriori algorithm is used for mining frequent item sets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability, while

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Handika, I. Putu Susila, and I. Kadek Susila Satwika. "PERBANDINGAN KINERJA ALGORITMA APRIORI DAN EQUIVALENCE CLASS TRANSFORMATION (ECLAT) DALAM MENEMUKAN POLA PEMBELIAN PADA DATA TRANSAKSI MINIMARKET." Networking Engineering Research Operation 9, no. 2 (2024): 149–60. https://doi.org/10.21107/nero.v9i2.28055.

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This study compares the performance of the Apriori and ECLAT algorithms in analyzing sales transaction data from a minimarket. The research focuses on examining both algorithms' efficiency in terms of execution time and memory usage when identifying frequent itemsets and generating association rules. Given the limited variety of products sold in a minimarket, a lower minimum support (0.001) and minimum confidence (0.005) were applied to ensure meaningful results, as higher thresholds resulted in no significant findings. The first test evaluated the time required to find frequent itemsets, reve

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Dissertations / Theses on the topic "Apriori algorithm"

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Gopal, Deepthi. "Rule Generation for Datasets with Ordinal Class Attributes." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037390.

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Olofsson, Niklas. "Implementation of the Apriori algorithm for effective item set mining in VigiBaseTM : Project report in Teknisk Fysik 15 hp." Thesis, Uppsala University, Department of Engineering Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-129688.

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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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Ravindranathan, Sampurna. "Identification of Discriminating Motifs in Heart Rate Time Series Data of Soccer Players." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535636263727764.

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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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Smékal, Luděk. "Získávání znalostí z textových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-412756.

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This MSc Thesis handles with so-called data mining. Data mining is about obtaining some data or informations from databases, where these data or informations are not directly visible, but they are accessible by using special algorithms. This MSc Thesis mainly aims documents clasifying by selected method in scope of digital library. The selected method is based on sets of items called "itemsets method". This method extends Apriori algorithm application field originally designed for transaction databases processing and generation of sets of frequented items.

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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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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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Hossain, Mahmud Shahriar. "Apriori approach to graph-based clustering of text documents." Thesis, Montana State University, 2008. http://etd.lib.montana.edu/etd/2008/hossain/HossainM0508.pdf.

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This thesis report introduces a new technique of document clustering based on frequent senses. The developed system, named GDClust (Graph-Based Document Clustering) [1], works with frequent senses rather than dealing with frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and uses an Apriori paradigm to find the frequent subgraphs, which reflect frequent senses. Discovered frequent subgraphs are then utilized to generate accurate sense-based document clusters. We propose a novel multilevel Gaussian minimum support strat

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ALMEIDA, Derciley Cunha de. "Descoberta automatizada de associações com o uso de algoritmo Apriori como técnica de mineração de dados." Universidade Federal de Goiás, 2011. http://repositorio.bc.ufg.br/tede/handle/tde/966.

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Made available in DSpace on 2014-07-29T15:08:17Z (GMT). No. of bitstreams: 1 Dissertacao Derciley Cunha de Almeida.pdf: 2389648 bytes, checksum: c4c207dc1855a4a0e99ee3eeed7c28b9 (MD5) Previous issue date: 2011-02-25<br>Nowadays, the use of modern information systems allows the storage and management of increasingly large amounts of data. On the other hand, the full analysis and the maximum extraction of useful information from this universe of available data present considerable challenges in view of inherent human limitations. This dissertation deals with the subject of data mining, which i

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Book chapters on the topic "Apriori algorithm"

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, et al. "Apriori Algorithm." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_27.

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Toivonen, Hannu. "Apriori Algorithm." In Encyclopedia of Machine Learning and Data Science. Springer US, 2023. http://dx.doi.org/10.1007/978-1-4899-7502-7_10-1.

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Toivonen, Hannu. "Apriori Algorithm." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_27.

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Gu, Jianlong, Baojin Wang, Fengyu Zhang, Weiming Wang, and Ming Gao. "An Improved Apriori Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23214-5_18.

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Zhou, Huaping, and Daoyi Zhang. "An Improved Apriori-Pro Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98776-7_10.

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Liao, Binhua. "An Improved Algorithm of Apriori." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04962-0_49.

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Pan, Ou. "Apriori Algorithm for Lacquer Classification." In Application of Intelligent Systems in Multi-modal Information Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74814-2_134.

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Xujing, Bi, and Xu Weixiang. "The Research of Improved Apriori Algorithm." In LISS 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32054-5_141.

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Anwar, M. A., Sayed Sayeed Ahmed, and Mohammad A. U. Khan. "Application of Apriori Algorithm on Examination Scores." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5243-4_24.

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Sharma, Neeraj Kumar, and N. K. Nagwani. "Study and Analysis of Incremental Apriori Algorithm." In High Performance Architecture and Grid Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22577-2_64.

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Conference papers on the topic "Apriori algorithm"

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Tiwari, Geeta, Shirish Mohan Dubey, Gaurav Sharma, and Apporva Bansal. "Modified Improved Apriori Algorithm for Reduced Time Complexity." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070961.

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Jiang, Guowei. "Improvement of Apriori Algorithm." In The fourth International Conference on Information Science and Cloud Computing. Sissa Medialab, 2016. http://dx.doi.org/10.22323/1.264.0016.

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Weixiao Liu, Junli Chen, Shifu Qu, and Wanggen Wan. "An improved Apriori algorithm." In IET International Communication Conference on Wireless Mobile & Computing (CCWMC 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.1930.

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Shi, Yongge, and Yiqun Zhou. "An Improved Apriori Algorithm." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.79.

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Rui Chang and Zhiyi Liu. "An improved apriori algorithm." In 2011 International Conference on Electronics and Optoelectronics (ICEOE). IEEE, 2011. http://dx.doi.org/10.1109/iceoe.2011.6013148.

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Bai, Sixue, and Xinxi Dai. "An Efficiency apriori Algorithm: P_Matrix Algorithm." In The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007). IEEE, 2007. http://dx.doi.org/10.1109/isdpe.2007.136.

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Changsheng, Zhang, Li Zhongyue, and Zheng Dongsong. "An Improved Algorithm for Apriori." In 2009 First International Workshop on Education Technology and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.227.

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Sarkan, Mehmet Onur, Aysel Akcakoca, Can Kucukakdag, and Zehra Cataltepe. "Alarm correlation using Apriori algorithm." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130156.

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Huiqi, Qiu. "Improvement parallelization in Apriori Algorithm." In CIPAE 2020: 2020 International Conference on Computers, Information Processing and Advanced Education. ACM, 2020. http://dx.doi.org/10.1145/3419635.3419712.

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Gang Yang, Hong Zhao, Lei Wang, and Ying Liu. "An implementation of improved apriori algorithm." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212246.

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