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Memory-efficient frequent-itemset mining
Proceedings of the 14th International Conference on Extending Database Technology, 2011 Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms---which operate entirely in main memory and avoid expensive disk accesses---and in particular the prefix tree-based algorithm FP-growth are generally among the most efficient of the available algorithms.
Benjamin Schlegel +2 more
openaire +3 more sources
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 +1 more
wiley +1 more source
Rekomendasi Paket Produk Guna Meningkatkan Penjualan Dengan Metode FP-Growth
Khazanah Informatika, 2018 Perilaku konsumen dalam membeli suatu produk memang sangat beragam. Ada pembeli yang gemar membeli produk yang telah dipaket, tetapi ada juga pembeli yang membeli produk yang mendapat diskon, dan masih banyak lagi.
Asrul Abdullah
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
Frequent itemset mining in high dimensional data: a review [PDF]
, 2018 This paper provides a brief overview of the techniques used in frequent itemset mining. It discusses the search strategies used; i.e. depth first vs. breadth-first, and dataset representation; i.e. horizontal vs. vertical representation.
Nurul Fariza Zulkurnain +3 more
core +1 more source
Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases
Mathematics, 2021 Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address ...
Gergely Honti, János Abonyi
doaj +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
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 +2 more
wiley +1 more source
Verified Programs for Frequent Itemset Mining
, 2018 International audienceFrequent itemset mining is one pillar of machine learning and is very important for many data mining applications. There are many different algorithms for frequent itemset mining, but to our knowledge no implementation has been ...
Whitney, Christopher +3 more
core +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