Results 181 to 190 of about 719,228 (260)

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts. [PDF]

Sci Adv
Ding R, Liu J, Hua K, Wang X, Zhang X, Shao M, Chen Y, Chen J.   +7 more
europepmc   +1 more source

Study on urban residents' travel mode choice based on the CART-Apriori method. [PDF]

Sci Rep
Song H, Wang X, Tian W, Shi L, Li S.
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

Temporal Evolution of the Profile of Patients Hospitalized with Heart Failure (2000-2022). [PDF]

Clin Pract
Seoane-Pillado T, Suárez-Gil R, Pértega-Díaz S, Piñeiro-Fernández JC, Rodriguez-Ameijeiras E, Casariego-Vales E.   +5 more
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

A survey of itemset mining

WIREs Data Mining and Knowledge Discovery, 2017
Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers.
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Bay Vo, Tin Chi Truong, Ji Zhang 0001, Hoai Bac Le   +5 more
semanticscholar   +4 more sources

Itemset Mining with Penalties

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016
We introduce a preferences-based itemset mining framework. Preferences are encoded by a penalty function over the transactions in a database. We define an itemset mining problem where we associate to each transaction a penalty value. This problem consists in generating the frequent itemsets with a maximum penalty threshold.
Jabbour, Said, Kaci, Souhila, Saïs, Lakhdar, Salhi, Yakoub   +3 more
openaire   +3 more sources

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Frequent itemset mining: A 25 years review

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2019
Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible for extracting frequently occurring events, patterns, or items in data.
JOSÉ Maria Luna, Philippe Fournier-Viger, Sebastian Ventura   +2 more
exaly   +2 more sources

Mining erasable itemsets with subset and superset itemset constraints

Expert Systems With Applications, 2017
Abstract Erasable itemset (EI) mining, a branch of pattern mining, helps managers to establish new plans for the development of new products. Although the problem of mining EIs was first proposed in 2009, many efficient algorithms for mining these have since been developed. However, these algorithms usually require a lot of time and memory usage.
Bay Võ, Tuong Le, Witold Pedrycz
exaly   +2 more sources

A Systematic Survey on High Utility Itemset Mining

International Journal of Information Technology and Decision Making, 2019
High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules.
Mohammad Karim Sohrabi
exaly   +2 more sources