Results 61 to 70 of about 10,877 (201)
A New Algorithm for High Average-utility Itemset Mining [PDF]
Journal of Artificial Intelligence and Data Mining, 2019 High utility itemset mining (HUIM) is a new emerging field in data mining which has gained growing interest due to its various applications. The goal of this problem is to discover all itemsets whose utility exceeds minimum threshold.
A. Soltani, M. Soltani
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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
Mining High-Efficiency Itemsets with Negative Utilities
MathematicsHigh-efficiency itemset mining has recently emerged as a new problem in itemset mining. An itemset is classified as a high-efficiency itemset if its utility-to-investment ratio meets or exceeds a specified efficiency threshold.
Irfan Yildirim
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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
An Efficient Approach for Mining High Average-Utility Itemsets in Incremental Database
Engineering ProceedingsTraditional high-utility itemset (HUI) mining methods tend to overestimate utility for long itemsets, leading to biased results. High average-utility itemset (HAUI) mining addresses this problem by normalizing utility with itemset length.
Ye-In Chang +2 more
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Majalah Ilmiah Teknologi Elektro, 2018 Algoritma FP-growth adalah algoritma data mining yang digunakan untuk menemukan frequent itemset pada data keranjang belanja. Frequent itemset adalah kelompok barang yang sering dibeli bersamaan dalam satu keranjang belanja. Analisa frequent itemset akan
I Gusti Agung Indrawan +2 more
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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
Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree
IEEE Access, 2020 Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on.
Jun-Feng Qu +5 more
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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 Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns
, 2015 Working with exhaustive search on large dataset is infeasible for several reasons. Recently, developed techniques that made pattern set mining feasible by a general solver with long execution time that supports heuristic search and are limited to small ...
Alam, Hasib Ul +2 more
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