Results 171 to 180 of about 2,690 (229)

Unveiling the Negative Customer Experience in Diagnostic Centers: A Data Mining Approach. [PDF]

J Multidiscip Healthc
Agarwal S, Singh R, Pandiya B, Bordoloi D.   +3 more
europepmc   +1 more source

Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil. [PDF]

Int J Environ Res Public Health
Marques JG, Carvalho BM, Guedes LA, Da Costa-Abreu M.   +3 more
europepmc   +1 more source

RETRACTED: Physical fitness characteristics and comprehensive physical fitness evaluation model of basketball players based on association rule algorithm.

PLoS One
Ding Y.
europepmc   +1 more source

Research on rapid construction methods and evaluation of health education resources in public health emergencies based on knowledge development. [PDF]

Front Public Health
Huang R, Zou Y, Zhou L, Jiang T.
europepmc   +1 more source

Diagnostic Features and Prescription Rules of Influenza-Like Illnesses in Traditional Chinese Medicine: A Data Mining Approach. [PDF]

J Multidiscip Healthc
Liu X, Chang R, Feng S, Deng G, Zhou L.
europepmc   +1 more source

AIM2 framework for smart marketing innovation using AI driven consumer analytics with SOR neural networks and XGBoost in Saudi retail. [PDF]

Sci Rep
Alarfaj FK, Badouch M, Khan HU, Boutaounte M.   +3 more
europepmc   +1 more source

Exploring Human Mobility: A Time-Informed Approach to Pattern Mining and Sequence Similarity. [PDF]

Int J Geogr Inf Sci
Yang H, Angela Yao X, Whalen CC, Kiwanuka N.   +3 more
europepmc   +1 more source

On Maximal Frequent Itemsets Mining with Constraints

, 2018
Recently, a new declarative mining framework based on constraint programming (CP) and propositional satisfiability (SAT) has been designed to deal with several pattern mining tasks. The itemset mining problem has been modeled using constraints whose models correspond to the patterns to be mined.
Saïd Jabbour, Fatima Ezzahra Mana, Imen Ouled Dlala, Badran Raddaoui, Lakhdar Sais   +4 more
openaire   +2 more sources

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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   +2 more sources

Mining Frequent and Homogeneous Closed Itemsets

2016
It is well known that when mining frequent itemsets from a transaction database, the output is usually too large to be effectively exploited by users. To cope with this difficulty, several forms of condensed representations of the set of frequent itemsets have been proposed, among which the notion of closure is one of the most popular.
Inès Hilali, Tao-Yuan Jen, Dominique Laurent 0001, Claudia Marinica, Sadok Ben Yahia   +4 more
openaire   +2 more sources