Academic literature on the topic 'Apriori-like algorithm'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Apriori-like algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Apriori-like algorithm"

1

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 d
APA, Harvard, Vancouver, ISO, and other styles
3

Alrahwan, Bana Ahmad, and Mona Farouk. "ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure." International Journal of Intelligent Systems 2024 (January 22, 2024): 1–16. http://dx.doi.org/10.1155/2024/8781318.

Full text
Abstract:
Data mining is the process used for extracting hidden patterns from large databases using a variety of techniques. For example, in supermarkets, we can discover the items that are often purchased together and that are hidden within the data. This helps make better decisions which improve the business outcomes. One of the techniques that are used to discover frequent patterns in large databases is frequent itemset mining (FIM) that is a part of association rule mining (ARM). There are different algorithms for mining frequent itemsets. One of the most common algorithms for this purpose is the Ap
APA, Harvard, Vancouver, ISO, and other styles
4

Trivedi, Jigisha, Kaushal Patel, Pooja Rathod, and Anuj Patel. "A Survey on Automated Support Threshold Based on Apriori Algorithm for Frequent Item sets." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26586.

Full text
Abstract:
Data mining is the technique to extract features from raw data. In Today’s era data mining has a lot of e- Commerce applications. It is widely used in a variety of application areas like banking, marketing and retail industry. The association rules generated from them are still important items. Apriori based algorithms tend to achieve high efficiency; when the database transactions are scarce.Study proposes an approach to deal with frequent item problem. Main goal is to provide an algorithm for frequent itemset mining with automated support thresholds. Apriori follows breadth search and bottom
APA, Harvard, Vancouver, ISO, and other styles
5

Hasibuan, Maria Hikmah, and M. Fakhriza. "Apriori Algorithm to Predict Availability of Beauty Products." Journal of Computer Networks, Architecture and High Performance Computing 6, no. 3 (2024): 1232–44. http://dx.doi.org/10.47709/cnahpc.v6i3.4259.

Full text
Abstract:
This study introduces the Apriori algorithm in beauty product availability prediction system as a solution to enhance stock prediction accuracy and mitigate inventory risks in the beauty industry. By applying data mining technology, specifically the Apriori algorithm, Kazana Kosmetik aims to gain insights into consumer purchasing patterns to optimize operations. The research analyzes transaction data to identify key buying patterns and improve stock management strategies. The results reveal seven main purchasing patterns with an average confidence value of 0.414, offering valuable guidance for
APA, Harvard, Vancouver, ISO, and other styles
6

Harshali Patil,, Et al. "Enhancing Retail Strategies through Apriori, ECLAT& FP Growth Algorithms in Market Basket Analysis." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 3831–38. http://dx.doi.org/10.17762/ijritcc.v11i9.9637.

Full text
Abstract:
"Market basket analysis" is a method employed in data mining to discover items that are commonly bought together by customers in a retail store. It is a crucial tool for retailers to understand consumer purchasing behavior and to improve their sales and marketing strategies. In this research paper, we present a comprehensive study on market basket analysis using three popular algorithms: Apriori, ECLAT, and FPGrowth. The paper begins with a brief synopsis of market basket analysis and the techniques adopted for itemset mining. We then introduce the dataset used in this study, which consists of
APA, Harvard, Vancouver, ISO, and other styles
7

Mahesh, Prabu Arunachalam. "Sentiment Analysis of Social Media Data for Product and Brand Evaluation: A Data Mining Approach Unveiling Consumer Preferences, Trends, and Insights." International Journal of Engineering and Management Research 14, no. 3 (2024): 46–52. https://doi.org/10.5281/zenodo.12541304.

Full text
Abstract:
Sentimental Analysis is an ongoing research field in Text Mining Arena to determine the situation of the market on particular entities such as Products, Services...Etc. This paper is a journal on sentiment analysis in social media that explores the methods, social media platforms used, and their application. It can be called a computational treatment of reviews, subjectivity, and sentiment. Social media contain a large amount of raw data that has been uploaded by users in the form of text, videos, photos, and audio. The data can be converted into valuable information by using sentiment analysi
APA, Harvard, Vancouver, ISO, and other styles
8

Ranjitha, B. R. "Recommendation System for Movie Cast and Crew using Datamining Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1495–97. https://doi.org/10.35940/ijeat.D7522.049420.

Full text
Abstract:
In this article, the data mining algorithms like apriori algorithm is used to suggest the best cast and crew to make a particular genre of movies so that the movie is successful. The data of cast and crew is extracted for which the users have given high average ratings and apply apriori algorithm to give recommendation. There are recommendation systems which gives the recommendation to the users so that the users can watch movies in which they are interested in. But there are no recommendation systems which gives the right information which is helpful for movie making. It is important to make
APA, Harvard, Vancouver, ISO, and other styles
9

He, Bo. "Fast Distributed Algorithm of Mining Global Frequent Itemsets." Advanced Materials Research 219-220 (March 2011): 191–94. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.191.

Full text
Abstract:
Most distributed algorithms of mining global frequent itemsets worked on net structure network and adopted Apriori-like algorithm. Whereas there were some problems in these algorithms: a lot of candidate itemsets and heavy communication traffic. Aiming at these problems, this paper proposed a fast distributed algorithm of mining global frequent itemsets, namely, FDMGFI algorithm, which set centre node. FDMGFI algorithm made computer nodes compute local frequent itemsets independently with FP-growth algorithm, then the centre node exchanged data with other computer nodes and combined, finally,
APA, Harvard, Vancouver, ISO, and other styles
10

Mustafa, Raniah Ali, Haitham Salman Chyad, and Jinan Redha Mutar. "Enhancement in privacy preservation in cloud computing using apriori algorithm." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (2022): 1747. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1747-1757.

Full text
Abstract:
Cloud <span>computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because of this factor cloud computing is emerging as in primary data storage for individuals as well as organisations. At the same time, privacy preservation is an also a significant aspect of cloud computing. In regrades to privacy preservation, association rule mining was proposed by previous researches to protect the privacy of users. However, the algorithm involves creation of fake transaction and this algorithm also fails to maintain the privacy of
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Apriori-like algorithm"

1

Chao, Ching-Ming, Po-Zung Chen, Shih-Yang Yang, and Cheng-Hung Yen. "An Efficient MapReduce-Based Apriori-Like Algorithm for Mining Frequent Itemsets from Big Data." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06158-6_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gupta, Usha, and Kamlesh Sharma. "Review of Data Mining Techniques Used in Healthcare." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6527-8.ch001.

Full text
Abstract:
Data mining plays a vital role in converting the medical data like text, image, and graphs into meaningful new data, which helps to take the better decision. In this chapter, an overview of the current research is discussed using the data mining techniques for the finding, analysis, and prediction of various diseases. The focus of this study is to identify the well-performing data mining algorithms used on medical and clinical databases. Multiple algorithms have been identified: text-based mining, association rule-based mining, pattern-based mining, keyword-based mining, machine learning, neural network support vector machine, apriori algorithm, k-means clustering, and natural language. Analyses of the algorithm show that there is no single algorithm or model more suitable for diagnosing or predicting diseases. In some scenarios, some algorithms work very well but not in another data set. There are many examples in clinical or medical research where the combination of different algorithms gives good results.
APA, Harvard, Vancouver, ISO, and other styles
3

Babu M., Vinaya, and Sreedevi Mooramreddy. "Performance Evaluation and Analysis of Different Association Rule Mining (ARM) Algorithms." In Handbook of Research on Advancements in AI and IoT Convergence Technologies. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6971-2.ch017.

Full text
Abstract:
Data processing technique of data mining has the power to identify patterns and relationships in huge volumes of data from multiple sources to make decisions to drive the world in the current scenario. Association rule mining (ARM) is the most significant method used in data mining. This approach is employed to find trends in the database that are typical. The field of ARM has seen a lot of activities. ARM remains a source of concern for various experts. There are algorithms that assess fundamental factors like precision, algorithm speed, and data assistance. The ARM algorithms, namely AprioriHybrid, AIS, AprioriTID, and Apriori, as well as FP-Growth, are examined in this work. This chapter provides a comparison of different algorithms utilized for association rules mining against several performance factors.
APA, Harvard, Vancouver, ISO, and other styles
4

Lee, Kah Win, Pantea Keikhosrokiani, Jia Hui Wong, and Moussa Pourya Asl. "Narrative Threads and Cinematic Connections Using Intelligent Systems to Enhance Movie Recommendations with Market Basket Analysis and Advanced Algorithms." In Advances in Business Information Systems and Analytics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1210-0.ch013.

Full text
Abstract:
Movie streaming services are businesses driven by data and strategies to predict future viewing patterns based on historical data. Without unsupervised learning techniques, industries like movie streaming services might face laborious tasks and issues in anticipating customer preferences and forecasting changes in customer behavior. In this chapter, market basket analysis (MBA) and recommender systems were implemented on MovieLens Data. In MBA, movie watching patterns were identified using two types of rule-generating algorithms, namely the apriori algorithm and the FP-growth algorithm. Three visualization idioms were generated to understand the association rules extracted in the MBA. Secondly, five types of recommender systems, namely memory-based collaborative filtering, model-based collaborative filtering, content filtering, context filtering, and the hybrid method, were implemented to suggest relevant movies to customers. Each recommender system was experimented with three different TopN configurations, and the results were evaluated using information retrieval metrics.
APA, Harvard, Vancouver, ISO, and other styles
5

Selvi, Dr R. Gangai. "BIG DATA ANALYTICS IN CROP YIELD PREDICTION OF TAMIL NADU (RICE)." In Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 5. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bact5p1ch2.

Full text
Abstract:
Rice holds significant importance as a staple food and cultivated crop, ranking as the third most valuable food crop globally, following wheat and sorghum. This study focuses on leveraging data analytics and machine learning techniques to analyse rice-related data and establish correlations between fixed attributes to predict crop yield. The dataset pertains to Rice cultivation in Tamil Nadu over a span of 20 years and includes factors like area, production, yield, temperature, rainfall, humidity, and wind speed. The dataset underwent preprocessing to facilitate the application of data analytics and machine learning algorithms. The K-Means clustering algorithm was utilized to categorize rice productivity data, while the apriori algorithm was employed to extract association rules from the processed data. To predict yield, a spatial regression method was also utilized. Based on the data analysis results, employing a predefined k=3 clusters, the crop yield data from 28 districts were grouped into three clusters based on their proximity to the nearest centroid. Furthermore, it was observed that districts grouped together exhibited similar rice production levels.By applying the apriori method to the rice dataset, with a minimum support of 0.001 and a confidence level of 90%, numerous association rules was generated. Among these, 31 pertinent rules were identified for achieving "High Yield Production”. The study optimized the districts' rice crop yield using both spatial and non-spatial regression models, validating the results using metrics like R2 and Root Mean Square Error. The study's primary outcome is a collection of well-defined and effective association rules that can facilitate yield prediction. These findings are anticipated to be valuable for researchers, farmers, and government authorities aiming to enhance rice crop productivity
APA, Harvard, Vancouver, ISO, and other styles
6

Koh, Yun Sing, and Nathan Rountree. "Rare Association Rule Mining." In Rare Association Rule Mining and Knowledge Discovery. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-754-6.ch001.

Full text
Abstract:
The notion of finding rare association rules is like finding precious gems in an open field; it is a daunting task but, if successful, it is very rewarding. Association rule mining systems, such as Apriori, generally employ an exhaustive search algorithm. While these algorithms are in theory capable of finding rare association rules, they become intractable if the minimum level of support is set low enough to find rare rules. Such algorithms are therefore inadequate for finding rare associations, and also suffer from the rare item problem. Research to solve this problem has become more prevalent in recent times. The main goal of rare association rule mining is to discover relationships among sets of items in a transactional database that occur infrequently. This chapter presents a survey on the current trends and approaches in the area of rare association rule mining.
APA, Harvard, Vancouver, ISO, and other styles
7

Messaoud, Riadh Ben, Sabine Loudcher Rabaséda, Rokia Missaoui, and Omar Boussaid. "OLEMAR." In Data Mining and Knowledge Discovery Technologies. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-960-1.ch001.

Full text
Abstract:
Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting information under different perspectives and levels of granularity. Nevertheless, OLAP techniques do not allow the identification of relationships, groupings, or exceptions that could hold in a data cube. To that end, we propose to enrich OLAP techniques with data mining facilities to benefit from the capabilities they offer. In this chapter, we propose an online environment for mining association rules in data cubes. Our environment called OLEMAR (online environment for mining association rules), is designed to extract associations from multidimensional data. It allows the extraction of inter-dimensional association rules from data cubes according to a sum-based aggregate measure, a more general indicator than aggregate values provided by the traditional COUNT measure. In our approach, OLAP users are able to drive a mining process guided by a meta-rule, which meets their analysis objectives. In addition, the environment is based on a formalization, which exploits aggregate measures to revisit the definition of the support and the confidence of discovered rules. This formalization also helps evaluate the interestingness of association rules according to two additional quality measures: lift and loevinger. Furthermore, in order to focus on the discovered associations and validate them, we provide a visual representation based on the graphic semiology principles. Such a representation consists in a graphic encoding of frequent patterns and association rules in the same multidimensional space as the one associated with the mined data cube. We have developed our approach as a component in a general online analysis platform called Miningcubes according to an Apriori-like algorithm, which helps extract inter-dimensional association rules directly from materialized multidimensional structures of data. In order to illustrate the effectiveness and the efficiency of our proposal, we analyze a real-life case study about breast cancer data and conduct performance experimentation of the mining process.
APA, Harvard, Vancouver, ISO, and other styles
8

Hsu, Wynne, Mong Li Lee, and Junmei Wang. "Mining Spatio-Temporal Graph Patterns." In Temporal and Spatio-Temporal Data Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-387-6.ch011.

Full text
Abstract:
Data mining in graph databases has received much attention. We have witnessed many algorithms proposed for mining frequent graphs. Inokuchi, Washio, and Nishimura (2002) and Karpis and Kumar (1998) introduce the Apriori-like algorithms, AGM and FSG, to mine the complete set of frequent graphs. However, both algorithms are not scalable as they require multiple scans of databases and tend to generate many candidates during the mining process. Subsequently, Yan and Han (2002) and Nijssen and Kok (2004) propose depth-first graph mining approaches called gSpan and Gaston, respectively. These approaches are essentially memory-based and their efficiencies decrease dramatically if the graph database is too large to fit into the main memory.
APA, Harvard, Vancouver, ISO, and other styles
9

Pathak, Laxmi Kumari, and Pooja Jha. "Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)." In Applications of Big Data in Large- and Small-Scale Systems. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6673-2.ch014.

Full text
Abstract:
Chronic kidney disease (CKD) is a disorder in which the kidneys are weakened and become unable to filter blood. It lowers the human ability to remain healthy. The field of biosciences has progressed and produced vast volumes of knowledge from electronic health records. Heart disorders, anemia, bone diseases, elevated potassium, and calcium are the very prevalent complications that arise from kidney failure. Early identification of CKD can improve the quality of life greatly. To achieve this, various machine learning techniques have been introduced so far that use the data in electronic health record (EHR) to predict CKD. This chapter studies various machine learning algorithms like support vector machine, random forest, probabilistic neural network, Apriori, ZeroR, OneR, naive Bayes, J48, IBk (k-nearest neighbor), ensemble method, etc. and compares their accuracy. The study aims in finding the best-suited technique from different methods of machine learning for the early detection of CKD by which medical professionals can interpret model predictions easily.
APA, Harvard, Vancouver, ISO, and other styles
10

Jabbour Said, Sais Lakhdar, Salhi Yakoub, and Tabia Karim. "Symmetries in Itemset Mining." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-098-7-432.

Full text
Abstract:
In this paper, we describe a new framework for breaking symmetries in itemset mining problems. Symmetries are permutations between items that leave invariant the transaction database. Such kind of structural knowledge induces a partition of the search space into equivalent classes of symmetrical itemsets. Our proposed framework aims to reduce the search space of possible interesting itemsets by detecting and breaking symmetries between items. Firstly, we address symmetry discovery in transaction databases. Secondly, we propose two different approaches to break symmetries in a preprocessing step by rewriting the transaction database. This approach can be seen as an original extension of the symmetry breaking framework widely used in propositional satisfiability and constraint satisfaction problems. Finally, we show that Apriori-like algorithms can be enhanced by dynamic symmetry reasoning. Our experiments clearly show that several itemset mining instances taken from the available datasets contain such symmetries. We also provide experimental evidence that breaking such symmetries reduces the size of the output on some families of instances.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Apriori-like algorithm"

1

Wei, Fang, and Georg Lausen. "A unified apriori-like algorithm for conjunctive query containment." In the 2008 international symposium. ACM Press, 2008. http://dx.doi.org/10.1145/1451940.1451957.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Thanh, Tran Thang, Fan Chen, Kazunori Kotani, and Bac Le. "An Apriori-like algorithm for automatic extraction of the common action characteristics." In 2013 Visual Communications and Image Processing (VCIP). IEEE, 2013. http://dx.doi.org/10.1109/vcip.2013.6706394.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gorawski, Martin, and Pawel Jureczek. "Using Apriori-like algorithms for spatio-temporal pattern queries." In 2009 International Multiconference on Computer Science and Information Technology (IMCSIT). IEEE, 2009. http://dx.doi.org/10.1109/imcsit.2009.5352771.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Tomovic, Savo, and Predrag Stanisic. "Upper bounds on the number of candidate itemsets in Apriori like algorithms." In 2014 3rd Mediterranean Conference on Embedded Computing (MECO). IEEE, 2014. http://dx.doi.org/10.1109/meco.2014.6862711.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!