Results 71 to 80 of about 659,904 (212)
Risk Analysis, Volume 46, Issue 1, January 2026.ABSTRACT Urban bus accidents present major safety and operational challenges, particularly in densely populated metropolitan areas. This study develops a machine learning‐based analytical framework to identify, quantify, and interpret the factors associated with severe bus accidents.
Bowei Chen +3 more
wiley +1 more source
A SURVEY ON ITEMSET MINING FOR LARGE TRANSACTION DATABASE
, 2016 Mining itemsets from the databases is an important data mining task.Frequent itemset mining refers to the mining of set of items occur frequently in the database.Utility itemset mining refers to the discovery of items with high utilities.
Ancy Jose*, Dr. John T Abraham
core +1 more source
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 +4 more
wiley +1 more source
Erasable Itemset Mining for Sequential Product Databases
, 2022 Data mining has become a popular research field in recent years. It is a crucial task to find meaningful information from large databases due to the current progress in networks and storage technology. There are various data mining tasks.
Chen, Yi-Li
core
LUIM: New Low-Utility Itemset Mining Framework
IEEE Access, 2019 High-utility itemset mining (HUIM), which is the detection of high-utility itemsets (HUIs) in a transactional database, provides the decision maker with greater flexibility to exploit item utilities, such as quantity and profits, to extract remarkable ...
Naji Alhusaini +5 more
doaj +1 more source
Complexity, Volume 2026, Issue 1, 2026.Frequent itemset mining (FIM) is a fundamental task in data mining with applications ranging from market basket analysis and recommendation systems to fraud detection and bioinformatics. However, mining frequent itemsets from massive datasets remains computationally challenging due to high execution time and memory consumption.
Rahat Ali Shah +7 more
wiley +1 more source
Verified Programs for Frequent Itemset Mining
, 2018 International audienceFrequent itemset mining is one pillar of machine learning and is very important for many data mining applications. There are many different algorithms for frequent itemset mining, but to our knowledge no implementation has been ...
Whitney, Christopher +3 more
core +1 more source
Health &Social Care in the Community, Volume 2026, Issue 1, 2026.Background Middle‐aged and older adults with severe mental illness (SMI) often experience a higher burden of multiple chronic conditions compared with the general population. Despite this, limited research has explored how these conditions cluster and interact, particularly in social settings where integrated mental and physical healthcare remains ...
Yue-Hui Yu, Ya-Xuan Mao, Quan Lu
wiley +1 more source
Erasable Itemset Mining with the Temporal Property
, 2021 Data mining is an approach to extracting meaningful or helpful patterns from a database to support decision making. Among various data mining problems, erasable-itemset mining is commonly utilized in production planning to distinguish the combinations of
Chang, Hao
core
TT-Miner: Topology-Transaction Miner for Mining Closed Itemset
IEEE Access, 2019 Mining frequent closed itemsets (FCIs) from transaction databases is a fundamental problem in many data mining applications. All the enumeration algorithms enumerate FCIs by adding a singleton item to an itemset and then checking whether it is closure ...
Bo Li +3 more
doaj +1 more source