Results 31 to 40 of about 739 (208)

Parallel Mining Algorithm for the Enumeration Space of Closed High Utility Itemsets

Journal of Harbin University of Science and Technology
To address the issues of result redundancy and time overhead in high-dimensional data environments, a closed high utility itemset mining algorithm, SpCHUIM (Closed High Utility Itemsets Mining on Spark), is proposed.
LI Chengyan, SUN Anqi, LIU Songlin
doaj   +1 more source

Closed Frequent Itemset Mining with Arbitrary Side Constraints [PDF]

2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018
Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on a small subset of the data.
Gokberk Kocak, Ozgur Akgun, Ian Miguel, Peter Nightingale   +3 more
openaire   +3 more sources

PARALLEL MINING OF FREQUENT MAXIMAL ITEMSETS USING ORDER PRESERVING GENERATORS [PDF]

ICTACT Journal on Soft Computing, 2010
In this paper, we propose a parallel algorithm for mining maximal itemsets. We propose POP-MAX (Parallel Order Preserving MAXimal itemset algorithm), a fast and memory efficient parallel algorithm which enumerates all the maximal patterns concurrently ...
R.V. Nataraj, S. Selvan
doaj  

Class Association Rule Pada Metode Associative Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2011
Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining.  Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list ...
Eka Karyawati, Edi Winarko
doaj   +1 more source

Double‐Edged Sword of Social Media Algorithms: Assessing the Risks to University Cybersecurity and Student Data Privacy

Engineering Reports, Volume 8, Issue 5, May 2026.
Social media algorithms drive a hidden risk chain: over‐disclosure → behavioral fusion → targeted attacks. We propose a 128‐dim, law‐aware risk scoring model with Drools‐based dynamic alerts for universities. ABSTRACT As universities undergo accelerated digital transformation, social media algorithms—while streamlining campus services—have emerged as a
Weishu Ye, Zhi Li
wiley   +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

Developing and Validating an Instrument for Assessing Secondary Students' Self‐Efficacy for Online Reading

Reading Research Quarterly, Volume 61, Issue 2, April/May/June 2026.
This study introduces and validates the Self‐Efficacy for Online Reading Questionnaire (SEORQ), a process‐grounded instrument designed to measure secondary students' efficacy in executing the core demands of online reading. The model conceptualizes online reading self‐efficacy as a multidimensional construct encompassing five interrelated processes ...
SeongYeup Kim, Yongjun Lee, Elena Forzani   +2 more
wiley   +1 more source

Weighted Association Rule Mining using Weighted Support and Significance Framework [PDF]

, 2003
We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to
Feng Tao, Murtagh, Fionn, Mohsen Farid, Tao, Feng, Fionn Murtagh, Farid, Mohsen   +5 more
core   +1 more source

A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM

IEEE Access
Frequent itemset mining (FIM) and high utility itemset mining (HUIM) are popular data mining techniques used in various real-world applications such as retail-market, bio-medicine, and click-stream analysis.
Muhammad Waheed Ashraf, M. Asif Naeem, Heejeong Jasmine Lee   +2 more
doaj   +1 more source

Personalized and Explainable Aspect‐Based Recommendation Using Latent Opinion Groups

Computational Intelligence, Volume 42, Issue 2, April 2026.
ABSTRACT The problem of explainable recommendation—supporting the recommendation of a product or service with an explanation of why the item is a good choice for the user—is attracting substantial research attention recently. Recommendations associated with an explanation of how the aspects of the chosen item may meet the needs and preferences of the ...
Maryam Mirzaei, Jörg Sander, Eleni Stroulia   +2 more
wiley   +1 more source