Results 41 to 50 of about 2,690 (229)

Generic Itemset Mining Based on Reinforcement Learning

IEEE Access, 2022
One of the biggest problems in itemset mining is the requirement of developing a data structure or algorithm, every time a user wants to extract a different type of itemsets.
Kazuma Fujioka, Kimiaki Shirahama
doaj   +1 more source

Critical Review for One‐Class Classification: Recent Advances and Reality Behind Them

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.
This review presents a new taxonomy to summarize one‐class classification (OCC) algorithms and their applications. The main argument is that OCC should not learn multiple classes. The paper highlights common violations of OCC involving multiple classes.
Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler   +3 more
wiley   +1 more source

Global Copper Deposit Dataset: A New Open‐Source Database for Advanced Data Analysis and Exploration Targeting

Geoscience Data Journal, Volume 13, Issue 1, January 2026.
We build a new, open‐source global copper deposit dataset (GCDD), facilitating AI‐driven data analysis for exploration targeting and improving our understanding of copper mineralizing systems and their mappable expressions. The GCDD hosts information about 1483 copper deposits worldwide, capturing key deposit attributes such as location, genetic type ...
Bin Wang, Renguang Zuo, Oliver P. Kreuzer   +2 more
wiley   +1 more source

Incremental Frequent Itemsets Mining With FCFP Tree

IEEE Access, 2019
Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression ...
Jiaojiao Sun, Yaling Xun, Jifu Zhang, Junli Li   +3 more
doaj   +1 more source

A primer to frequent itemset mining for bioinformatics [PDF]

Briefings in Bioinformatics, 2013
Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping ...
Stefan Naulaerts, Pieter Meysman, Wout Bittremieux, Trung-Nghia Vu, Wim Vanden Berghe, Bart Goethals, Kris Laukens   +6 more
openaire   +4 more sources

A Design‐Driven Machine Learning Approach for Invariant Mining in a Smart Grid

IET Cyber-Physical Systems: Theory &Applications, Volume 11, Issue 1, January/December 2026.
An ICS is vulnerable to cyber‐attacks arising from within its communication network or directly from the SCADA and devices such as PLCs. The study reported here presents a scenario‐specific invariant mining approach to detect anomalies in plant behaviour.
Danish Hudani, Muhammad Haseeb, Muhammad Taufiq, Muhammad Azmi Umer, Nandha Kumar Kandasamy, Aditya P. Mathur   +5 more
wiley   +1 more source

Extraction of Safe Operation Rules and Identification of Vulnerable Nodes in Power Grids Based on Time‐Series Association Analysis

IET Generation, Transmission &Distribution, Volume 20, Issue 1, January/December 2026.
This paper presents a data‐driven framework for operational safety rule extraction and vulnerable node identification in power grids with high renewable penetration. The effectiveness of the proposed method is verified on the IEEE 39‐bus system for static security assessment. ABSTRACT High renewable energy penetration introduces significant uncertainty
Zhilin Huang, Zheng Xu, Yudun Li, Yanqi Hou, Facai Xing, Zhi Gao, Yiping Yu   +6 more
wiley   +1 more source

Mining top-K frequent itemsets through progressive sampling

, 2010
We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very ...
PIETRACAPRINA, ANDREA ALBERTO, UPFAL E, VANDIN, FABIO, RIONDATO M   +3 more
core   +1 more source

An association rule-based approach for frequent item mining of multi-stage access data

Discover Computing
The processing of large-scale datasets is complex and requires high efficiency. The database needs to be scanned multiple times by traditional Apriori algorithms to generate candidate itemsets, resulting in significantly reduced efficiency, but also have
Silong Wu
doaj   +1 more source

From Prediction to Prevention: Using Text Mining and Explainable Machine Learning for Urban Bus Accident Analytics

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, Yufei Huang, Yu Zheng, Xiaofeng Liu   +3 more
wiley   +1 more source