Results 151 to 160 of about 1,488,747 (207)

Incremental high average-utility itemset mining: survey and challenges. [PDF]

Sci Rep
Chen J, Yang S, Ding W, Li P, Liu A, Zhang H, Li T.   +6 more
europepmc   +1 more source

Ontology-driven association rule mining for biomedical entity relationships: integrating hierarchical knowledge to improve gene-disease discovery. [PDF]

Sci Rep
Naqash MA, Amin M, Uddin J, Hussein HS, Raza A, Alghamdi W, Mostafa HA, Alkahtani HK.   +7 more
europepmc   +1 more source

Risk identification and assessment of Internet public opinion on public emergencies based on Bayesian network and association rule mining. [PDF]

Front Public Health
You M, Pan X, Zhu C.
europepmc   +1 more source

Artificial intelligence guided acupuncture decision making and treatment: a review of research. [PDF]

Front Med (Lausanne)
Nie R, Deng K, He J, Jia J, Wang H, Yuan S, Li T.   +6 more
europepmc   +1 more source

Web log mining techniques to optimize Apriori association rule algorithm in sports data information management. [PDF]

Sci Rep
Li T, Liu F, Chen X, Ma C.
europepmc   +1 more source

Mining Complex Ecological Patterns in Protected Areas: An FP-Growth Approach to Conservation Rule Discovery. [PDF]

Entropy (Basel)
Hunyadi ID, Cismaș C.
europepmc   +1 more source

Verified Programs for Frequent Itemset Mining

2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018
Frequent 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 proven correct using computer aided verification. Hu et al. derived on paper an efficient algorithm for this problem, starting
Frédéric Loulergue, Christopher D. Whitney   +1 more
openaire   +4 more sources

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Frequent Itemset Mining

2019
We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Marco Pulimeno, Massimo Cafaro
exaly   +3 more sources

Memory issues in frequent itemset mining

Proceedings of the 2004 ACM symposium on Applied computing, 2004
During the past decade, many algorithms have been proposed to solve the frequent itemset mining problem, i.e. find all sets of items that frequently occur together in a given database of transactions. Although very efficient techniques have been presented, they still suffer from the same problem. That is, they are all inherently dependent on the amount
Goethals, Bart, Bart Goethals
openaire   +3 more sources

Frequent Itemset Mining for Big Data

2013 IEEE International Conference on Big Data, 2013
Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem.
Sandy Moens, Emin Aksehirli, Bart Goethals   +2 more
openaire   +3 more sources