Results 161 to 170 of about 2,811 (218)

Disease spectrum and comorbidity patterns of malignant neoplasms: a multi-center hospital-based retrospective analysis of inpatient insurance claims data. [PDF]

Front Oncol
Liu M, Chen L, Du P, Wei C, Ding T, Liang L, Wu J, Zeng Z.   +7 more
europepmc   +1 more source

Subset binding enables detection of multimodal patient subgroup patterns and drug target discovery in idiopathic pulmonary fibrosis. [PDF]

Brief Bioinform
Natsume-Kitatani Y, Itoh MN, Takeda Y, Kuroda M, Hirata H, Miyake K, Shiroyama T, Shirai Y, Noda Y, Adachi Y, Enomoto T, Amiya S, Adachi J, Narumi R, Muraoka S, Tomonaga T, Kurohashi S, Cheng F, Tanaka R, Yada S, Aramaki E, Wakamiya S, Chen YA, Fukagawa A, Higuchi C, Nojima Y, Fujiwara T, Nagao C, Takeda T, Matsumura Y, Mizuguchi K, Kumanogoh A, Ueda N.   +32 more
europepmc   +1 more source

Effective Noise Reduction in NDR Systems: A Simple Yet Powerful Apriori-Based Approach. [PDF]

Sensors (Basel)
Homayoun S, Haraldsdóttir M, Lynge E, Jensen CD.   +3 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

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

Enhancing employee performance appraisal through optimized association rule algorithms: a data mining approach. [PDF]

Sci Rep
Wang J.
europepmc   +1 more source

Patterns Discovery Dataset for Particulate Matter (PM_2.5) Pollution Trends in Japan. [PDF]

Sci Data
Rage UK, Kattumuri V, Pogaku AC.
europepmc   +1 more source

Optimization algorithm of association rule mining for heavy-haul railway freight train fault data based on distributed parallel computing.

Sci Prog
Bai Y, Li H, Wang W, Liu S, Zhang N, Zhang C.   +5 more
europepmc   +1 more source

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.
Cafaro, Massimo, Pulimeno, Marco
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Efficient algorithms for deriving complete frequent itemsets from frequent closed itemsets

Applied Intelligence, 2021
When mining frequent itemsets (abbr. FIs) from dense datasets, it usually produces too many itemsets and results in the mining task to suffer from a very long execution time and high memory consumption. Frequent closed itemset (abbr. FCI) is a compact and lossless representation of FI. Mining FCIs can not only reduce the execution time and memory usage,
Cheng-Wei Wu, JianTao Huang, Yun-Wei Lin, Chien-Yu Chuang, Yu-Chee Tseng   +4 more
openaire   +1 more source