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

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Data mining; the rules of association; apriori algorithm'

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Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Data mining; the rules of association; apriori algorithm"

1

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 connected, and the key word identifying is adopted in the candidate set, thus the process of pruning and string pattern matching is removed from Apriori algorithm. Original algorithm and improved algorithm implementation steps are presented by examples, the results show that the new algorithm reduces the storage space, improve the efficiency of the algorithm and improve the performance of data mining technology.
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Tong, Yu Jun, Jun Zhou, Wen Ge Xie, and Dan Jia. "Research and Application of an Enhanced Data Mining Algorithm in Virtual Manufacturing Technology." Advanced Materials Research 299-300 (July 2011): 840–43. http://dx.doi.org/10.4028/www.scientific.net/amr.299-300.840.

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Association rules mining is an important branch of data mining. Apriori algorithm is a classical algorithm of mining association rules. Based on the original Apriori algorithm an improved Apriori algorithm is analyzed according to the multiple minimum supports and support difference constraint. An experiment has been conducted and the results showed that the new algorithm can not only mine out the association rules to meet the demands of multiple minimum supports, but also mine out the rare but potentially profitable items’ association rules.
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Wang, Pei Ji, and Yu Lin Zhao. "Research on Data Mining Based on Apriori Algorithm." Advanced Materials Research 532-533 (June 2012): 1675–79. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1675.

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With the availability of inexpensive storage and the progress in data collection tools, many organizations have created large databases of business and scientific data, which create an imminent need and great opportunities for mining interesting knowledge from data.Mining association rules is an important topic in the data mining research. In the paper, research mining frequent itemsets algorithm based on recognizable matrix and mining association rules algorithm based on improved measure system, the above method is used to mine association rules to the students’ data table under Visual FoxPro 6.0.
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Chauhan, Harvinder, and Anu Chauhan. "Implementation of the Apriori algorithm for association rule mining." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 04 (2014): 699–701. https://doi.org/10.5281/zenodo.14715587.

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With massive amounts of data continuously being collected and stored, many industries are becoming interested in mining association rules from their databases. The discovery of interesting association relationships among huge amounts of business transaction records can help in many business decision mak ing processes. Association rule mining contains some set of algorithms, whenever we mine the rules we have to use the algorithms. Weka, a software tool for data mining tasks contains the famous algorithm known as Apriori algorithm for association rule mining which computes all rules that have a given minimum support and exceed a given confidence. In this paper we are implementing Apriori algorithm using “weather data set” from weka. This paper also gives insights into the association rules mined by this algorithm in the implementation section. 
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Lawal, Ma’aruf Mohammed, and Ogedengbe Tunde Matthew. "FP-Growth Algorithm: Mining Association Rules without Candidate Sets Generation." Kasu Journal of Computer Science 1, no. 2 (2024): 392–411. http://dx.doi.org/10.47514/kjcs/2024.1.2.0016.

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Over the years, due to modern technological advancement, unprecedented volume of data is been captured, and this has necessitated the need to mine such data to provide decision-based solution to non-trivial problems. Deploying an efficiently critical decision-based solution for handling such problems, require data mining algorithms. These evolving techniques emerged as an indispensable tools for pattern discovery in inventory data. With one notable technique being the application of Association Rule analysis, especially the Market Basket Analysis. However, mining association rules from large datasets can be daunting due to the volume of candidate sets generated by association rule algorithms like Apriori and ECLAT. Thus, candidate sets generated by these association rule based algorithms yield numerous rules, which contain both interesting and uninteresting ones. Hence, making interpretation overwhelming and decision-making challenging. On this note, this paper focused on demonstrating the efficiency of the FP-Growth algorithm in extracting relevant and interesting association rules for mining transaction itemsets over large datasets. By examining the FP-Growth algorithm design, functionality, and performance in depth analysis. The FP-Growth algorithm, which is an improved version of the Apriori algorithm is introduced with the intent to reduce the overhead costs by employing the FP-Tree data structure that efficiently encode the frequency information of itemsets in a dataset. To demonstrate performance improvement of the FP-Growth over the Apriori algorithms, the two algorithm were implemented on the WEKA data mining platform using a supermarket dataset. The performance of both algorithms is evaluated and compared in terms of computational time. The experimental results shows that the FP-Growth algorithm recorded 82.04% improvement over the Apriori algorithm. This improvement is attributed to the FP-Growth algorithm single dataset scan and the absence of candidate set generation which is inherent in the Apriori algorithm.
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Johan, Ragil Andika, Rispani Himilda, and Nadya Auliza. "PENERAPAN METODE ASSOCIATION RULE UNTUK STRATEGI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI." Jurnal Teknik Informatika (J-Tifa) 2, no. 2 (2019): 1–7. http://dx.doi.org/10.52046/j-tifa.v2i2.268.

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Abstrak
 Persaingan dalam bisnis khususnya dalam bisnis perdagangan semakin banyak. Agar dapat meningkatkan penjualan produk yang dijual, para pelaku harus mempunyai strategi. Salah satu cara yang bisa dilakukan adalah dengan memanfaatkan data transaksi penjualan. Data penjualan tersebut dapat diolah hingga didapatkan informasi yang berguna bagi peningkatan penjualan. Teknologi yang dapat digunakan dalam hal ini adalah data mining. Data mining adalah kegiatan pengolahan data untuk menemukan hubungan dalam suatu data yang berjumlah besar. Suatu metode yang dapat digunakan dalam data mining adalah association rule mining. Association rule mining adalah salah satu metode data mining yang dapat mengidentifikasi hubungan kesamaan antar item. Algoritma yang paling sering dipakai dalam metode ini salah satunya ialah algoritma apriori. Algoritma apriori digunakan untuk mencari kandidat aturan asosiasi. Aturan kombinasi produk berhasil ditemukan dengan penerapan metode assosiation rules menggunakan algoritma apriori dan telah diuji menggunakan tools tanagra. Semua rule yang dihasilkan pada penelitian ini memiliki nilai lift ratio lebih dari 1 sehingga dapat digunakan sebagai acuan dalam membuat strategi penjualan.
 Kata Kunci : Penjualan, Data Mining, Association Rule, Algoritma Apriori
 
 Abstract
 Competition in business, especially in the trading business more and more. In order to increase sales of the products, businessman must have a strategy. A things we can do is to use sales transaction data. The sales data can be processed so we will get information of increasing sales. The technology that can be used in this case is data mining. Data mining, often also called knowledge discovery in database (KDD), is a data processing activity to find relationships in a large amount of data. A method that can be used in data mining is association rule mining. Association rule mining is one method of data mining that can identify the similarity relationships between items. One of the most frequently used algorithms in this method is the apriori algorithm. Apriori algorithm is used to find candidate association rules. The product combination rules have been found by applying the association rules method using apriori algorithm and have been tested using tanagra tools. All rules produced in this study have a lift ratio value of more than 1 so it can be used as a reference in making sales strategies.
 Keywords: Sale, Rule Mining, Association Rule, Apriori Algorithm
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Ji, Hai Peng, Tai Yong Wang, Jing Liu, Shi Yan Fan, Zhi Peng Wang, and Kai Ran Zhang. "An Efficient Parallel Association Rules Mining Algorithm for Fault Diagnosis." Key Engineering Materials 693 (May 2016): 1326–30. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1326.

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With the development of Internet industry, equipment data is increasing. The traditional method is not suitable for processing large data. Aiming at inefficient problem of Apriori algorithm when mining very large database, an efficient parallel association rules mining algorithm (Advanced Pruning Parallel Apriori Algorithm) based on a cluster is presented. APPAA algorithm can enhance the mining efficiency, as well as the system’s extension. Experimental results show that APPAA algorithm cuts down 85% mining time of Apriori, and it has good characteristics of parallel and expandable.so it is suitable for mining very large size database of fault diagnosis.
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Yan, Chun, Jiahui Liu, Wei Liu, and Xinhong Liu. "Research on automobile insurance fraud identification based on fuzzy association rules." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 5821–34. http://dx.doi.org/10.3233/jifs-201301.

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With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims.
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Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.

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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.
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Rakhimova, L.S. "PERFORMANCE ANALYSIS OF ASSOCIATION RULE MINING ALGORITHMS USING HADOOP." EURASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES 2, no. 14 (2022): 43–47. https://doi.org/10.5281/zenodo.7478791.

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Association rule mining has been a very important method in the field of data mining. Apriori algorithm is a classical algorithm for association rule mining. In the big data environment, the traditional Apriori algorithm has been unable to meet the needs of mining. In the paper, the parallelization of the Apriori algorithm is implemented based on the Hadoop platform and the Map Reduce programming model. On the basis, the algorithm is further optimized by using the idea of transaction reduction. Experimental results show that the proposed algorithm can be better to meet the requirements of big data mining and efficiently mining frequent itemsets and association rules from large dataset.
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Dissertations / Theses on the topic "Data mining; the rules of association; apriori algorithm"

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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 number of rules most of which are redundant. Therefore we implement a three phase elimination process on the generated rules to come up with a reasonably small set of interesting rules. The approach is applied for two different data sets of the company, one for production defects and one for raw material non-conformities. We then validate the resultant rules using a test data set for each problem type and analyze the final set of rules.
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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 algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup.
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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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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 a framework providing a representation of patterns in relational databases, refinement methods of patterns, and primitives for obtaining necessary record counts from database to calculate measures for patterns. The framework exploits meta-data of relational databases for pruning search space of patterns. The implementation extends the framework by employing Apriori algorithm for further pruning the search space and discovering relational recursive patterns. Apriori algorithm is used for finding large itemsets of tables, which are used to refine patterns. Apriori algorithm is modified by changing support calculation method for itemsets. A method for determining recursive relations is described and a solution is provided for handling recursive patterns using aliases. Additionally, continuous attributes of tables are discretized utilizing equal-depth partitioning. The implementation is tested with gene localization prediction task of KDD Cup 2001 and results are compared to those of the winner approach.
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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 Dados (Data Mining), que consiste na aplicação de algoritmos e técnicas estatísticas para explorar informações contidas implicitamente em grandes bancos de dados. Muitas áreas utilizam o processo KDD para facilitar o reconhecimento de padrões ou modelos em suas bases de informações. Este trabalho apresenta uma aplicação prática do processo KDD utilizando a base de dados de alunos do 9 ano do ensino básico do Estado do Rio de Janeiro, disponibilizada no site do INEP, com o objetivo de descobrir padrões interessantes entre o perfil socioeconômico do aluno e seu desempenho obtido em Matemática na Prova Brasil 2011. Neste trabalho, utilizando-se da ferramenta chamada Weka (Waikato Environment for Knowledge Analysis), foi aplicada a tarefa de mineração de dados conhecida como associação, onde se extraiu regras por intermédio do algoritmo Apriori. Neste estudo foi possível descobrir, por exemplo, que alunos que já foram reprovados uma vez tendem a tirar uma nota inferior na prova de matemática, assim como alunos que nunca foram reprovados tiveram um melhor desempenho. Outros fatores, como a sua pretensão futura, a escolaridade dos pais, a preferência de matemática, o grupo étnico o qual o aluno pertence, se o aluno lê sites frequentemente, também influenciam positivamente ou negativamente no aprendizado do discente. Também foi feita uma análise de acordo com a infraestrutura da escola onde o aluno estuda e com isso, pôde-se afirmar que os padrões descobertos ocorrem independentemente se estes alunos estudam em escolas que possuem infraestrutura boa ou ruim. Os resultados obtidos podem ser utilizados para traçar perfis de estudantes que tem um melhor ou um pior desempenho em matemática e para a elaboração de políticas públicas na área de educação, voltadas ao ensino fundamental.<br>Nowadays, most of the transactions made by companies and organizations is stored in databases that can be explored by researchers in order to obtain useful information to aid decision making. Due to the large volume involved, the extraction and analysis of data is not a simple task. The general process of converting raw data into useful information is called Knowledge Discovery in Databases (KDD). One step in this process is the Data Mining, which involves the application of algorithms and statistical techniques to exploit information contained implicitly in large databases. Many areas use the KDD process to facilitate the recognition of patterns or models on their bases of information. This work presents a practical application of KDD process using the database of students in the 9th grade of elementary education in the State of Rio de Janeiro, available in INEP site, with the aim of finding interesting patterns between the socioeconomic profile of the student and his/her performance obtained in Mathematics. The tool called Weka was used and the Apriori algorithm was applied to extracting association rules. This study revealed, for example, that students who have been reproved once tend to get a lower score on the math test, as well as students who had never been disapproved have had superior performance. Other factors like student future perspectives, ethnic group, parent's schooling, satisfaction in mathematics studying, and the frequency of access to Internet also affect positively or negatively the students learning. An analysis related to the schools infrastructure was made, with the conclusion that patterns do not change regardless of the student studying in good or bad infrastructure schools. The results obtained can be used to trace the students profiles which have a better or a worse performance in mathematics and to the development of public policies in education, aimed at elementary education.
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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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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 utilizados, contendo, cada um deles, um conjunto de implementações algorítmicas e de diversas técnicas de data mining, além de serem softwares de domínio público. Regras conhecidas foram encontradas (confirmadas pelo especialista médico em análise de eletrocardiograma), evidenciando a validade dessa metodologia.<br>In this study, the exploration of electrocardiograms (ECG) databases, obtained from a Tele-ECG System of Dante Pazzanese Institute of Cardiology, has been proposed, applying the technique of data mining to find patterns that could collaborate, in the future, for the acquisition of knowledge in the analysis of electrocardiograms. The proposed method was to investigate the data looking for patterns without the use of the ECG traces. Three Data-mining open source software packages (Weka, Orange and R - Project) were used, containing, each one, a set of algorithmic implementations and various data mining techniques, as well as being a public domain software. Known rules were found (confirmed by medical experts in electrocardiogram analysis), showing the validity of the methodology.
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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 distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.
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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 distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients' records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients' personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.
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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 data mining algorithms for estimating frequent sub-sequences and association rules. The dissertation proposes a new stochastic algorithm for mining frequent sub-sequences, its modifications SDPA1 and SDPA2 and stochastic algorithm for discovery of association rules, and presents the evaluation of the algorithm errors. These algorithms are approximate, but allow us to combine two important tests, i.e. time and accuracy. The algorithms have been tested using real and simulated databases.<br>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 dažnų posekių paieškos algoritmas, jo modifikacijos SDPA1, SDPA2 ir stochastinis susietumo taisyklių nustatymo algoritmas bei pateiktas šių algoritmų paklaidų įvertinimas. Šie algoritmai yra apytiksliai, tačiau leidžia suderinti du svarbius kriterijus  laiką ir tikslumą. Šie algoritmai buvo testuojami naudojant realias bei imitacines duomenų bazes.
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Book chapters on the topic "Data mining; the rules of association; apriori algorithm"

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Adamo, Jean-Marc. "Apriori and Other Algorithms." In Data Mining for Association Rules and Sequential Patterns. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_3.

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Rai, Amrita, Rohit Kumar, and Navneet Kumar. "Association rule mining using Apriori algorithm based on big data mining technology." In Advances in Electronics, Computer, Physical and Chemical Sciences. CRC Press, 2025. https://doi.org/10.1201/9781003616252-73.

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Rehman, Shabnum, and Anil Sharma. "Privacy Preserving Data Mining Using Association Rule Based on Apriori Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5780-9_20.

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Zhou, Xian, and Hai Huang. "A Data Mining and Processing Method for E-Commerce Potential Customers Based on Apriori Association Rules Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50546-1_13.

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Oikonomou, Maria G., Apostolos Ziakopoulos, Shanna Lucchesi, Monica Olyslagers, and George Yannis. "Traffic Simulation and Safety Assessment Requirements for Enhancing Road Safety Prediction Tools." In Lecture Notes in Mobility. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85578-8_109.

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Abstract Improving road safety prediction tools requires assessing established traffic simulation tools and safety assessment methods. Enhancing these tools with innovative data sources and methods can significantly reduce urban crashes and their impact. To achieve this, it is imperative to identify the requirements and gaps of relevant stakeholders in terms of professional road safety analysis tools. The present study aims to utilize association rule mining to determine underlying profiles of local stakeholders who are identified as hands-on practitioners. To accomplish this objective, a dedicated survey was conducted, and the data were analyzed to discover meaningful links among stakeholder characteristics through the characteristics mined using the Apriori algorithm. The results provide a quantification of the frequency and relationships between stakeholder responses, indicating connections between education levels, work regions, experience levels, and stakeholder needs related to road safety prediction tools. The study insights offer a quantitative perspective on the interconnections and dependencies among different stakeholder attributes, shedding light on potential patterns and preferences that can guide decision-making in the context of road safety improvements.
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Mehta, Anjali, and Deepa Bura. "Mining of Association Rules in R Using Apriori Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_14.

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Liang, Xu, Caixia Xue, and Ming Huang. "Improved Apriori Algorithm for Mining Association Rules of Many Diseases." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16388-3_30.

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Mata, Jacinto, José-Luis Alvarez, and José-Cristobal Riquelme. "Discovering Numeric Association Rules via Evolutionary Algorithm." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47887-6_5.

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Diaz, Jorge, David Ovallos-Gazabon, and Carlos Vargas Mercado. "Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm." In Proceedings of International Conference on Big Data, Machine Learning and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4788-5_20.

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Li, Xin, Zhi-Hong Deng, and Shiwei Tang. "A Fast Algorithm for Maintenance of Association Rules in Incremental Databases." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_5.

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Conference papers on the topic "Data mining; the rules of association; apriori algorithm"

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Chen, Xiang, Minyu Luo, Weilang Song, Fan Pan, Hongling Chen, and Dan Lin. "Research on Power Outage Data Mining Pattern using Apriori-based Association Rule Algorithm." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035758.

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Wu, Haotong. "Data Association Rules Mining Method Based on Improved Apriori Algorithm." In ICBDR 2020: 2020 the 4th International Conference on Big Data Research. ACM, 2020. http://dx.doi.org/10.1145/3445945.3445948.

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Wu, Xiaodong, and Yuzhu Zeng. "Using Apriori Algorithm on Students’ Performance Data for Association Rules Mining." In Proceedings of the 2nd International Seminar on Education Research and Social Science (ISERSS 2019). Atlantis Press, 2019. http://dx.doi.org/10.2991/iserss-19.2019.156.

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Wu, Xiaodong, and Yuzhu Zeng. "Using Apriori Algorithm on Students’ Performance Data for Association Rules Mining." In Proceedings of the 2nd International Seminar on Education Research and Social Science (ISERSS 2019). Atlantis Press, 2019. http://dx.doi.org/10.2991/iserss-19.2019.300.

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Cong, Yi. "Research on Data Association Rules Mining Method Based on Improved Apriori Algorithm." In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020. http://dx.doi.org/10.1109/icbase51474.2020.00085.

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Zhang, Xueting. "Study of an improved Apriori algorithm for data mining of association rules." In 2015 International conference on Applied Science and Engineering Innovation. Atlantis Press, 2015. http://dx.doi.org/10.2991/asei-15.2015.238.

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Gong, Peng, Chi Yang, Hui Li, and Weili Kou. "The Application of Improved Association Rules Data Mining Algorithm Apriori in CRM." In 2007 2nd International Conference on Pervasive Computing and Applications. IEEE, 2007. http://dx.doi.org/10.1109/icpca.2007.4365419.

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Dongre, Jugendra, Gend Lai Prajapati, and S. V. Tokekar. "The role of Apriori algorithm for finding the association rules in Data mining." In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2014. http://dx.doi.org/10.1109/icicict.2014.6781357.

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Harikumar, Sandhya, and Divya Usha Dilipkumar. "Apriori algorithm for association rule mining in high dimensional data." In 2016 International Conference on Data Science and Engineering (ICDSE). IEEE, 2016. http://dx.doi.org/10.1109/icdse.2016.7823952.

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Ren, Xiang. "Application of Apriori Association Rules Algorithm to Data Mining Technology to Mining E-commerce Potential Customers." In 2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021. http://dx.doi.org/10.1109/iwcmc51323.2021.9498773.

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