Results 51 to 60 of about 2,690 (229)

GPU-Accelerated Apriori Algorithm

ITM Web of Conferences, 2017
This paper propose a parallel Apriori algorithm based on GPU (GPUApriori) for frequent itemsets mining, and designs a storage structure using bit table (BIT) matrix to replace the traditional storage mode. In addition, parallel computing scheme on GPU is
Jiang Hao, Xu Chen-Wei, Liu Zhi-Yong, Yu Li-Yan   +3 more
doaj   +1 more source

Correlation Analysis of Influencing Factors of Autonomous Vehicle Accidents Based on Improved Apriori Algorithm

Journal of Advanced Transportation, Volume 2026, Issue 1, 2026.
The purpose of this study was to explore the risk factors for autonomous vehicle (AV) crashes and their interdependencies. A total of 659 AV crash data were collected between 2018 and July 2024 from AV crash reports published by the California Department of Motor Vehicles.
Tao Wang, Wenzhi Tang, Juncong Chen, Wenwu Chen, Mohammad Rezwan Habib   +4 more
wiley   +1 more source

Inverted Index Automata Frequent Itemset Mining for Large Dataset Frequent Itemset Mining

IEEE Access
Frequent itemset mining (FIM) faces significant challenges with the expansion of large-scale datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor scalability and low efficiency when applied to modern datasets characterized by high dimensionality and high-density features.
Xin Dai 0007, Haza Nuzly Abdull Hamed, Qichen Su, Xue Hao   +3 more
openaire   +2 more sources

Identifying the Focus Word in Natural Language Questions Based on Association Rules

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
Knowledge base‐based intelligent question‐answering systems have insufficient understanding of the questions. In the early stages of research, it is effective in most cases that the existing natural language question‐understanding methods can answer questions by connecting entities and relationships when ignoring the identification of focus words ...
Xin Hu, Xiaofeng Ren, Jian Zheng, Jiangli Duan, Sulan Zhang, Richard Murray   +5 more
wiley   +1 more source

Efficiently Mining Maximal Diverse Frequent Itemsets [PDF]

, 2019
Given a database of transactions, where each transaction is a set of items, maximal frequent itemset mining aims to find all itemsets that are frequent, meaning that they consist of items that co-occur in transactions more often than a given threshold ...
Wu, Dingming, Luo, Dexin, Huang, Joshua Zhexu, Dexin Luo, Jensen, Christian S.; id_orcid, Dingming Wu, Christian S. Jensen, Joshua Zhexue Huang   +7 more
core   +1 more source

Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

IEEE Access
Traditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates.
Xiumei Zhao, Xincheng Zhong, Bing Han
doaj   +1 more source

FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

IEEE Access, 2020
Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task.
Tianyou Wei, Bin Wang, Yuntian Zhang, Keyong Hu, Yinfeng Yao, Hao Liu   +5 more
doaj   +1 more source

Taming the Triangle: On the Interplays Between Fairness, Interpretability, and Privacy in Machine Learning

Computational Intelligence, Volume 41, Issue 4, August 2025.
ABSTRACT Machine learning techniques are increasingly used for high‐stakes decision‐making, such as college admissions, loan attribution, or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias and do not leak sensitive information ...
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie‐José Huguet, Mohamed Siala   +4 more
wiley   +1 more source

CLOLINK: An Adapted Algorithm for Mining Closed Frequent Itemsets

, 2012
Mining of the complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of closed frequent itemsets, which results in a much smaller number of itemsets.
Onashoga, Adebukola, Adebukola Onashoga
core   +1 more source

Frequent Itemset Mining and Association Rules [PDF]

, 2006
With the advent of mass storage devices, databases have become larger and larger. Point-of-sale data, patient medical data, scientific data, and credit card transactions are just a few sources of the ever-increasing amounts of data. These large datasets provide a rich source of useful information.
Imberman S., Tansel A.U.
openaire   +3 more sources