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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, Hsu, Ching-Chi, Chen, Henry   +2 more
openaire   +2 more sources

Somatic Multimorbidity in Adults With Severe Mental Illness Relative to a General‐Population Reference in China: Implications for Integrated Community Care

Health &Social Care in the Community, Volume 2026, Issue 1, 2026.
Background Middle‐aged and older adults with severe mental illness (SMI) often experience a higher burden of multiple chronic conditions compared with the general population. Despite this, limited research has explored how these conditions cluster and interact, particularly in social settings where integrated mental and physical healthcare remains ...
Yue-Hui Yu, Ya-Xuan Mao, Quan Lu
wiley   +1 more source

Mining frequent itemsets a perspective from operations research [PDF]

Many papers on frequent itemsets have been published. Besides somecontests in this field were held. In the majority of the papers the focus ison speed. Ad hoc algorithms and datastructures were introduced.
Kosters, W.A., Pijls, W.H.L.M.
core  

A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM

IEEE Access
Frequent itemset mining (FIM) and high utility itemset mining (HUIM) are popular data mining techniques used in various real-world applications such as retail-market, bio-medicine, and click-stream analysis.
Muhammad Waheed Ashraf, M. Asif Naeem, Heejeong Jasmine Lee   +2 more
doaj   +1 more source

Identifying the Focus Word in Natural Language Questions Based on Association Rules

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
Knowledge base‐based intelligent question‐answering systems have insufficient understanding of the questions. In the early stages of research, it is effective in most cases that the existing natural language question‐understanding methods can answer questions by connecting entities and relationships when ignoring the identification of focus words ...
Xin Hu, Xiaofeng Ren, Jian Zheng, Jiangli Duan, Sulan Zhang, Richard Murray   +5 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, Nele Dexters, Toon Calders, Bart Goethals   +3 more
core  

Mining Frequent Itemsets in Correlated Uncertain Databases

, 2015
Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of uncertain data, e.g., RFID data, sensor data, real-time video data, has been collected.
Chen, Lei, Tong, Yong Xin, She, Jieying
core   +1 more source

Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets

The Scientific World Journal, 2014
Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets ...
Sajid Mahmood, Muhammad Shahbaz, Aziz Guergachi   +2 more
doaj   +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

Taming the Triangle: On the Interplays Between Fairness, Interpretability, and Privacy in Machine Learning

Computational Intelligence, Volume 41, Issue 4, August 2025.
ABSTRACT Machine learning techniques are increasingly used for high‐stakes decision‐making, such as college admissions, loan attribution, or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias and do not leak sensitive information ...
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie‐José Huguet, Mohamed Siala   +4 more
wiley   +1 more source