Results 51 to 60 of about 8,898 (200)

Mining frequent closed itemsets out of core [PDF]

Proceedings of the 2006 SIAM International Conference on Data Mining, 2006
Extracting frequent itemsets is an important task in many data mining applications. When data are very large, it becomes mandatory to perform the mining task by using an external memory algorithm, but only a few of these algorithms have been proposed so far.
LUCCHESE, Claudio, ORLANDO, Salvatore, R. PEREGO   +2 more
openaire   +3 more sources

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

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

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 ...
Andrea Pietracaprina, E Cohen, Eli Upfal, Fabio Vandin, J Wang, M Charikar, M Mitzenmacher, Matteo Riondato, RC-W Wong   +8 more
core   +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

An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

Journal of Safety Science and Resilience
The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently.
M. Sinthuja, S. Pravinthraja, B K Dhanalakshmi, H L Gururaj, Vinayakumar Ravi, G Jyothish Lal   +5 more
doaj   +1 more source

Mining frequent itemsets with convertible constraints [PDF]

Proceedings 17th International Conference on Data Engineering, 2002
Recent work has highlighted the importance of the constraint based mining paradigm in the context of frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases. The authors study constraints which cannot be handled with existing theory and techniques.
null Jian Pei, null Jiawei Han, L.V.S. Lakshmanan   +2 more
openaire   +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

Mining Frequent Itemsets Using Genetic Algorithm

, 2010
In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent ...
Biswas, Sushanta, Ghosh, Soumadip, Sarkar, Debasree, Sarkar, Partha Pratim   +3 more
core   +2 more sources

Efficiently mining maximal frequent itemsets

Proceedings 2001 IEEE International Conference on Data Mining, 2002
We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation.
K. Gouda, M.J. Zaki
openaire   +1 more source