Results 61 to 70 of about 2,597 (181)
Content Validity of Creativity Self‐Report Questionnaires From PISA 2022
The Journal of Creative Behavior, Volume 59, Issue 2, June 2025.ABSTRACT The present paper questions the content validity of the eight creativity‐related self‐report scales available in PISA 2022's context questionnaire and provides a set of considerations for researchers interested in using these indexes. Specifically, we point out some threats to the content validity of these scales (e.g., creative thinking self ...
B. Goecke, S. Weiss, B. Barbot
wiley +1 more source
MINING FREQUENT itemsets using advanced partition APPROACH [PDF]
, 2009 Frequent itemsets mining plays an important part in many data mining tasks. This technique has been used in numerous practical applications, including market basket analysis. This paper presents mining frequent itemsets in large database of medical sales
Khin Myat Myat Moe +3 more
core
Incremental Frequent Itemsets Mining With FCFP Tree
IEEE Access, 2019 Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression ...
Jiaojiao Sun +3 more
doaj +1 more source
Health Science Reports, Volume 8, Issue 1, January 2025.ABSTRACT Background and Aims Infertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule‐mining methods, to analyze diverse patient data and uncover relevant ...
Hosna Heydarian +3 more
wiley +1 more source
Mining frequent closed itemsets with the frequent pattern list [PDF]
Proceedings 2001 IEEE International Conference on Data Mining, 2002 The mining of a complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of frequent closed itemsets (FCIs), which results in a much smaller number of itemsets. The approaches to mining frequent closed itemsets can be categorized into two groups: those with candidate generation and
Tseng, Fan-Chen +2 more
openaire +2 more sources
IET Communications, Volume 19, Issue 1, January/December 2025.This paper proposed an Automated Quality Assessment of SRS (AQA‐SRS) framework by integrating four popular methods which are; NLP, K‐means, MAS, and CBR to assess the quality of SRS documents. The NLP utilize for feature extraction, K‐means for features clustering, MAS for interactive assessment and feature selection decision, and CBR for managing the ...
Mohammed Ahmed Jubair +6 more
wiley +1 more source
An AI knowledge‐based system for police assistance in crime investigation
Expert Systems, Volume 42, Issue 1, January 2025.Abstract The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law ...
Carlos Fernandez‐Basso +4 more
wiley +1 more source
Mining frequent itemsets in a stream, in: [PDF]
, 2007 Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rapid succession and storing parts of the stream is typically impossible. Nonetheless, it has many useful applications; e.g.
Joris J M Gillis +3 more
core
A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream
, 2011 Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining.
Ning Zhang, Hai Feng Li
core +1 more source
ENHANCED ALGORITHMS FOR MINING OPTIMIZED POSITIVE AND NEGATIVE ASSOCIATION RULE FROM CANCER DATASET
ICTACT Journal on Soft Computing, 2018 The most important research aspect nowadays is the data. Association rule mining is vital mining used in data which mines many eventual informations and associations from enormous databases.
I Berin Jeba Jingle, J Jeya ACelin
doaj +1 more source