Results 221 to 230 of about 402,340 (284)

Unveiling meropenem resistance and co-resistance patterns in Klebsiella pneumoniae and Acinetobacter baumannii: a global genome analysis using ML/DL and association mining. [PDF]

BMC Microbiol
Ramachandran S, K D, M S, R MR, S A, V A, P DD, B S, Mohan S S.   +8 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

Cardiovascular risk prediction and influencing predictors identification among Bangladeshi individuals using machine learning algorithms and association rule mining. [PDF]

PLoS One
Islam MM, Kumar S, Salam MA, Roy DC, Karim MR.   +4 more
europepmc   +1 more source

Sex-Specific Risk Factors for Dynapenia in Korean Middle-Aged and Older Adults: A Cross-Sectional Study Based on the Korea National Health and Nutrition Examination Survey 2014-2019. [PDF]

J Pers Med
Yu H, Kim HJ, Choi H, Kim C, Lee JJ.
europepmc   +1 more source

Physical fitness characteristics and comprehensive physical fitness evaluation model of basketball players based on association rule algorithm. [PDF]

PLoS One
Ding Y.
europepmc   +1 more source

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Mining generalized association rules

Future Generation Computer Systems, 1997
Abstract We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy.
Ramakrishnan Srikant, Rakesh Agrawal
openaire   +3 more sources

Mining Causal Association Rules

2013 IEEE 13th International Conference on Data Mining Workshops, 2013
Discovering causal relationships is the ultimate goal of many scientific explorations. Causal relationships can be identified with controlled experiments, but such experiments are often very expensive and sometimes impossible to conduct. On the other hand, the collection of observational data has increased dramatically in recent decades.
Jiuyong Li, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, Bingyu Sun   +5 more
openaire   +2 more sources

Association Rule Mining

2009
Data mining is a field encompassing study of the tools and techniques to assist humans in intelligently analyzing (mining) mountains of data. Data mining has found successful applications in many fields including sales and marketing, financial crime identification, portfolio management, medical diagnosis, manufacturing process management and health ...
Vasudha Bhatnagar, Sarabjeet Kochhar
openaire   +2 more sources

Association Rule Mining

2005
Association Rule Mining (ARM) is concerned with how items in a transactional database are grouped together. It is commonly known as market basket analysis, because it can be likened to the analysis of items that are frequently put together in a basket by shoppers in a market. From a statistical point of view, it is a semiautomatic technique to discover
WOON, Yew-Kwong, NG, Wee-Keong, LIM, Ee Peng   +2 more
openaire   +1 more source

Mining association rules from quantitative data☆

Intelligent Data Analysis, 1999
Data-mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values, however, transactions with quantitative values are commonly seen in real-world applications.
Hong, Tzung-Pei, Kuo, Chan-Sheng, Chi, Sheng-Chai   +2 more
openaire   +2 more sources