Results 81 to 90 of about 659,904 (212)
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 +5 more
wiley +1 more source
PaWI: Parallel Weighted Itemset Mining by means of MapReduce [PDF]
, 2015 Frequent itemset mining is an exploratory data mining technique that has fruitfully been exploited to extract recurrent co-occurrences between data items.
Luigi Grimaudo +7 more
core +1 more source
A Java Library for Itemset Mining with Choco-solver
, 2023 While traditional data mining techniques have been used extensively for discovering patterns in databases, they are not always suitable for incorporating user-specified constraints.
Loudni, Samir, Vernerey, Charles
core +1 more source
PARALLEL MINING OF FREQUENT MAXIMAL ITEMSETS USING ORDER PRESERVING GENERATORS [PDF]
ICTACT Journal on Soft Computing, 2010 In this paper, we propose a parallel algorithm for mining maximal itemsets. We propose POP-MAX (Parallel Order Preserving MAXimal itemset algorithm), a fast and memory efficient parallel algorithm which enumerates all the maximal patterns concurrently ...
R.V. Nataraj, S. Selvan
doaj
An Efficient Method for Mining Closed Potential High-Utility Itemsets
IEEE Access, 2020 High-utility itemset mining (HUIM) has become a key phase of the pattern mining process, which has wide applications, related to both quantities and profits of items. Many algorithms have been proposed to mine high-utility itemsets (HUIs).
Bay Vo +5 more
doaj +1 more source
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 +4 more
wiley +1 more source
Memory-efficient frequent-itemset mining
Proceedings of the 14th International Conference on Extending Database Technology, 2011 Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms---which operate entirely in main memory and avoid expensive disk accesses---and in particular the prefix tree-based algorithm FP-growth are generally among the most efficient of the available algorithms.
Benjamin Schlegel +2 more
openaire +3 more sources
An Evolutionary Algorithm to Mine High-Utility Itemsets
Advances in Electrical and Electronic Engineering, 2015 High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) of association rules (ARs). In this
Jerry Chun-Wei Lin +5 more
doaj +1 more source
High Voltage, Volume 10, Issue 3, Page 710-725, June 2025.Abstract Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer, thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies. However, existing prediction methods based on the structure of ‘splicing prediction and diagnosis method’ suffer ...
Peng Zhang +5 more
wiley +1 more source
Frequent itemset mining: technique to improve eclat based algorithm [PDF]
, 2019 In frequent itemset mining, the main challenge is to discover relationships between data in a transactional database or relational database. Various algorithms have been introduced to process frequent itemset.
Jalil, Masita Abdul +3 more
core +1 more source