Results 51 to 60 of about 1,488,747 (207)
A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM
IEEE AccessFrequent 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 +2 more
doaj +1 more source
Globally Valid Rule‐Based Explanations for Black‐Box Models
Computational Intelligence, Volume 42, Issue 4, August 2026.ABSTRACT Black‐box explanation methods such as Lime define a neighborhood around the query example, learn an interpretable local surrogate model in this neighborhood, and use this local surrogate model for its explanation. Lore and Anchors are two such methods, which deliver local explanations in the form of IF‐THEN rules.
Van Quoc Phuong Huynh +3 more
wiley +1 more source
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
Journal of Safety Science and ResilienceThe numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently.
M. Sinthuja +5 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 +2 more
wiley +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 +3 more
wiley +1 more source
Frequent itemset mining on multiprocessor systems [PDF]
, 2013 Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis.
Schlegel, Benjamin
core +3 more sources
Frequent Itemset Mining and Association Rules [PDF]
, 2006 With the advent of mass storage devices, databases have become larger and larger. Point-of-sale data, patient medical data, scientific data, and credit card transactions are just a few sources of the ever-increasing amounts of data. These large datasets provide a rich source of useful information.
Imberman S., Tansel A.U.
openaire +3 more sources
Annals of Neurology, Volume 99, Issue 1, Page 231-247, January 2026.Objective Behavior and motor manifestations are distinctive yet often overlooked features of epileptic seizures. Seizures can result in transient disruptions in motor control, often organized into specific behavioral sequences that can inform seizure types, onset zones, and outcomes.
Yuyan Shen +8 more
wiley +1 more source
Accelerating Parallel Frequent Itemset Mining on Graphics Processors with Sorting
, 2013 Part 4: Session 4: Multi-core Computing and GPUInternational audienceFrequent Itemset Mining (FIM) is one of the most investigated fields of data mining.
Hsu, Ching-Hsien +9 more
core +1 more source