1. NiaAutoARM: automated framework for constructing and evaluating association rule mining pipelinesUroš Mlakar, Iztok Fister, Iztok Fister, 2025, izvirni znanstveni članek Opis: Numerical Association Rule Mining (NARM), which simultaneously handles both numerical and categorical attributes, is a powerful approach for uncovering meaningful associations in heterogeneous datasets. However, designing effective NARM solutions is a complex task involving multiple sequential steps, such as data preprocessing, algorithm selection, hyper-parameter tuning, and the definition of rule quality metrics, which together form a complete processing pipeline. In this paper, we introduce NiaAutoARM, a novel Automated Machine Learning (AutoML) framework that leverages stochastic population-based metaheuristics to automatically construct full association rule mining pipelines. Extensive experimental evaluation on ten benchmark datasets demonstrated that NiaAutoARM consistently identifies high-quality pipelines, improving both rule accuracy and interpretability compared to baseline configurations. Furthermore, NiaAutoARM achieves superior or comparable performance to the state-of-the-art VARDE algorithm while offering greater flexibility and automation. These results highlight the framework’s practical value for automating NARM tasks, reducing the need for manual tuning, and enabling broader adoption of association rule mining in real-world applications.
Ključne besede: AutoML, association rule mining, numerical association rule mining, pipelines
Objavljeno v DKUM: 16.06.2025; Ogledov: 0; Prenosov: 0
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2. Using generative AI to improve the performance and interpretability of rule-based diagnosis of Type 2 diabetes mellitusLeon Kopitar, Iztok Fister, Gregor Štiglic, 2024, izvirni znanstveni članek Opis: Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy.
Ključne besede: GPT, association rule mining, classification, interpretability, diagnostics
Objavljeno v DKUM: 26.11.2024; Ogledov: 0; Prenosov: 250
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3. Time series numerical association rule mining variants in smart agricultureIztok Fister, Dušan Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz, 2023, izvirni znanstveni članek Ključne besede: association rule mining, smart agriculture, optimization, evolutionary algotihms, internet of things
Objavljeno v DKUM: 12.06.2024; Ogledov: 121; Prenosov: 14
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4. Action-Based Digital Characterization of a Game PlayerDamijan Novak, Domen Verber, Jani Dugonik, Iztok Fister, 2023, izvirni znanstveni članek Ključne besede: association rule mining, digital characterization, game agent, game player, real-time strategy games
Objavljeno v DKUM: 23.05.2024; Ogledov: 131; Prenosov: 8
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5. Variable-length differential evolution for numerical and discrete association rule miningUroš Mlakar, Iztok Fister, Iztok Fister, 2023, izvirni znanstveni članek Opis: This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.
Ključne besede: association rule mining, differential evolution, data mining, variable-lenght solution representation, green AI
Objavljeno v DKUM: 18.01.2024; Ogledov: 341; Prenosov: 26
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6. uARMSolver: a framework for association rule miningIztok Fister, Iztok Fister, 2020, elaborat, predštudija, študija Ključne besede: association rule mining, categorical attributes, numerical attributes, software framework, optimization
Objavljeno v DKUM: 17.03.2021; Ogledov: 1446; Prenosov: 41
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