Results 41 to 50 of about 15,856,028 (211)

An Evolutionary Algorithm to Mine High-Utility Itemsets

Advances in Electrical and Electronic Engineering, 2015
High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) of association rules (ARs). In this
Jerry Chun-Wei Lin, Lu Yang, Philippe Fournier-Viger, Jaroslav Frnda, Lukas Sevcik, Miroslav Voznak   +5 more
doaj   +1 more source

Incrementally Updating the Discovered High Average-Utility Patterns With the Pre-Large Concept

IEEE Access, 2020
High average-utility itemset mining (HAUIM) is an extension of high-utility itemset mining (HUIM), which provides a reliable measure to reveal utility patterns by considering the length of the mined pattern.
Jimmy Ming-Tai Wu, Qian Teng, Jerry Chun-Wei Lin, Chien-Fu Cheng   +3 more
doaj   +1 more source

Efficient heuristics for the Steiner forest problem

International Transactions in Operational Research, EarlyView.
Abstract Let G=(V,E)$G = (V, E)$ be a connected undirected graph, V$V$ a set of nodes, E$E$ a set of edges, |V|=n$|V| = n$, and |E|=m$|E| = m$. Given a non‐negative weight function w:E→R+$w: E \rightarrow \mathbb {R}^+$ associated with its edges, a set τ={Ti⊆V|i=1,…,p}$\tau = \lbrace T_i \subseteq V | i = 1, \ldots, p\rbrace$ of terminal sets Ti$T_i ...
Murilo Stockinger, Isabel Rosseti, Simone Martins, Luidi Simonetti, Alexandre Plastino   +4 more
wiley   +1 more source

EHAUPM: Efficient High Average-Utility Pattern Mining With Tighter Upper Bounds

IEEE Access, 2017
High-utility itemset mining (HUIM) has become a popular data mining task, as it can reveal patterns that have a high-utility, contrarily to frequent pattern mining, which focuses on discovering frequent patterns.
Jerry Chun-Wei Lin, Shifeng Ren, Philippe Fournier-Viger, Tzung-Pei Hong   +3 more
doaj   +1 more source

Consumer Behavior Analysis in Digital Marketing Using AI and Big Data Analytics: A Narrative Review and Methodological Taxonomy

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
AI and Big Data in Consumer Behavior Analysis. ABSTRACT The rapid expansion of digital consumer data has challenged traditional approaches to understanding behavior in digital marketing. Existing reviews often focus on individual methods and give limited guidance on how analytical techniques compare or how they should be selected for specific marketing
Leonidas Theodorakopoulos, Alexandra Theodoropoulou   +1 more
wiley   +1 more source

arules - A Computational Environment for Mining Association Rules and Frequent Item Sets [PDF]

, 2005
Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Bettina Grün, Kurt Hornik, Michael Hahsler, Michael Hahsler, Hahsler, Michael, Hornik, Kurt, Grün, Bettina   +6 more
core   +1 more source

Polypharmacy burden and incident epilepsy among older adults in the United States

Epilepsia Open, Volume 11, Issue 4, Page 1370-1381, August 2026.
Abstract Objectives To estimate the prevalence of polypharmacy among older adults with incident epilepsy and to describe the most common combinations of drug classes filled prior to epilepsy diagnosis. Polypharmacy—the concurrent use of multiple medications—is common in older adults with epilepsy, but little is known about its burden and specific ...
Galen Shearn‐Nance, Long Vu, Hannah L. Fein, Wyatt P. Bensken, Jorden G. Rieke, Martha Sajatovic, Gena Ghearing, David F. Warner, Siran M. Koroukian, Nicholas K. Schiltz   +9 more
wiley   +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

A Survey on Discovering High Utility Itemset Mining from Transactional Database [PDF]

, 2016
Data Mining is the process of evaluating data from different outlooks and summarizing it into useful information. It can be defined as the process that extracts information contained in very large database.
Patel, Shekhar, Madhushree, B
core   +1 more source

Mining Correlated High Utility Itemsets in One Phase

IEEE Access, 2020
High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM
Bay Vo, Le V. Nguyen, Vinh V. Vu, Mi T. H. Lam, Thuy T. M. Duong, Ly T. Manh, Thuy T. T. Nguyen, Loan T. T. Nguyen, Tzung-Pei Hong   +8 more
doaj   +1 more source