1. Using data mining to improve decision-making : case study of a recommendation system developmentHyrmet Mydyti, Arbana Kadriu, Mirjana Pejić Bach, 2023, original scientific article Abstract: Background and purpose: This study aims to provide a practical perspective on how data mining techniques are used in the home appliance after-sales services. Study investigates on how can a recommendation system help a customer service company that plans to use data mining to improve decision making during its digital transformation process. In addition, study provides a detailed outline on the process for developing and analyzing platforms to improve data analytics for such companies. Methodology: Case study approach is used for evaluating the usability of recommendation systems based on data mining approach in the context of home appliance after-sales services. We selected the latest platforms based on their relevance to the recommender system and their applicability to the functionality of the data mining system as trends in the system design. Results: Evaluation of the impact on decision making shows how the application of data mining techniques in organizations can increase efficiency. Evaluation of the time taken to resolve the complaint, as a key attribute of service quality that affects customer satisfaction, and the positive results achieved by the recommendation system are presented. Conclusion: This paper increases the understanding of the benefits of the data mining approach in the context of recommender systems. The benefits of data mining, an important component of advanced analytics, lead to an increase in business productivity through predictive analytics. For future research, other attributes or factors useful for the recommender systems can be considered to improve the quality of the results. Acknowledgement: The author Hyrmet Mydyti’s PhD thesis has been extended in this paper.
Keywords: digital transformation, data mining, decision tree algorithm, decision-making, home appliances after-sales services
Published in DKUM: 08.10.2025; Views: 0; Downloads: 2
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2. A novel framework for unification of association rule miningRahul Sharma, Minakshi Kaushik, Sijo Arakkal Peious, Alexandre Bazin, Syed Attique Shah, Iztok Fister, Sadok Ben Yahia, Dirk Draheim, 2022, original scientific article Abstract: Statistical reasoning was one of the earliest methods to draw insights from data. However, over the last three decades, association rule mining and online analytical processing have gained massive ground in practice and theory. Logically, both association rule mining and online analytical processing have some common objectives, but they have been introduced with their own set of mathematical formalizations and have developed their specific terminologies. Therefore, it is difficult to reuse results from one domain in another. Furthermore, it is not easy to unlock the potential of statistical results in their application scenarios. The target of this paper is to bridge the artificial gaps between association rule mining, online analytical processing and statistical reasoning. We first provide an elaboration of the semantic correspondences between their foundations, i.e., itemset apparatus, relational algebra and probability theory. Subsequently, we propose a novel framework for the unification of association rule mining, online analytical processing and statistical reasoning. Additionally, an instance of the proposed framework is developed by implementing a sample decision support tool. The tool is compared with a state-of-the-art decision support tool and evaluated by a series of experiments using two real data sets and one synthetic data set. The results of the tool validate the framework for the unified usage of association rule mining, online analytical processing, and statistical reasoning. The tool clarifies in how far the operations of association rule mining and online analytical processing can complement each other in understanding data, data visualization and decision making.
Keywords: association rule mining, data mining, online analytical processing, statistical reasoning
Published in DKUM: 02.10.2025; Views: 0; Downloads: 2
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3. Mapping the evolution of social innovation in scientific publications : a topic modelling and text mining approachUroš Godnov, Jana Hojnik, Simona Kustec, 2025, original scientific article Abstract: Objective: To trace how academic discourse on social innovation has evolved from 2000 – mid-2024 in numbers and leading topics by applying a special topic modelling and text mining methodology. Data & Sources: 4,703 full-text journal articles retrieved from Science Direct. Methods: Literature review and PDF text extracted with PyPDF2 and pdfplumber; cleaned and tokenised in R; topic modelling performed with Latent Dirichlet Allocation (ldatuning-optimised); temporal and correlation analyses visualised via tidyverse. Results: The number of publications increased significantly from 16 (in 2000) to 573 (in 2021), stabilizing thereafter. Seven dominant topics emerged: renewable energy, environmental/resource management, smart-city governance, sustainable food systems, corporate strategy, academic-method studies, and social-governance structures. “Social” and “innovation” became the top word pair after 2006; energy-related terms surged after 2016. Surprisingly, topics typically considered ‘social’ have not dominated the social innovation discourse in scientific communities compared to the aforementioned dominant topics. Discussion: Our results largely confirm existing findings from literature reviews and affirm the interdisciplinary, vague, contested, and still intensively evolving nature of social innovation. Dominant social innovation topics in scientific papers reference to social innovation topics in global political and policy documents, notably from the EU (from 2013 onwards) and the 2015 UN SDGs agenda, also emphasising collaboration between scientific, business, political and non-governmental stakeholders, and can thus serve as scientific, evidence-based advocacy for other stakeholders involved in social innovation processes. Conclusions: Social innovation research is now an established, systemic, and broadly interdisciplinary field of study, focusing on sustainability, emerging technologies, and governance topics. It is tightly connected with the political and policy agendas of leading international organisations, as well as business and non-governmental ones. Implications: Findings guide scholars to under-explored social-related content and niches (such as governance and, especially, equity topics) and help policymakers and other stakeholders involved in social innovation processes locate evidence-based approaches and clusters when designing their socially innovative responses, interventions, solutions, and measures.
Keywords: social innovation theories, global policy agenda, text mining, topic modelling, literature review
Published in DKUM: 05.09.2025; Views: 0; Downloads: 2
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4. Toward explainable time-series numerical association rule mining : a case study in smart-agricultureIztok Fister, Sancho Salcedo-Sanz, Enrique Alexandre-Cortizo, Damijan Novak, Iztok Fister, Vili Podgorelec, Mario Gorenjak, 2025, original scientific article Abstract: This paper defines time-series numerical association rule mining in smart-agriculture applications from an explainable-AI perspective. Two novel explainable methods are presented, along with a newly developed algorithm for time-series numerical association rule mining. Unlike previous approaches, such as fixed interval time-series numerical association, the proposed methods offer enhanced interpretability and an improved data science pipeline by incorporating explainability directly into the software library. The newly developed xNiaARMTS methods are then evaluated through a series of experiments, using real datasets produced from sensors in a smart-agriculture domain. The results obtained using explainable methods within numerical association rule mining in smart-agriculture applications are very positive.
Keywords: association rule mining, explainable artificial intelligence, XAI, numerical association rule mining, optimization algorithms
Published in DKUM: 27.08.2025; Views: 0; Downloads: 5
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5. NiaAutoARM: automated framework for constructing and evaluating association rule mining pipelinesUroš Mlakar, Iztok Fister, Iztok Fister, 2025, original scientific article Abstract: 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.
Keywords: AutoML, association rule mining, numerical association rule mining, pipelines
Published in DKUM: 16.06.2025; Views: 0; Downloads: 1
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6. Synergy of blockchain technology and data mining techniques for anomaly detectionAida Kamišalić Latifić, Renata Kovačević, Iztok Fister, 2021, review article Abstract: Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing
an important role in the modern Information Technology (IT) revolution. Blockchain has recently
been popularized by the rise of cryptocurrencies, while data mining has already been present in IT
for many decades. Data stored in a blockchain can also be considered to be big data, whereas data
mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this
paper presents the interplay of these two research areas. In this paper, we surveyed approaches for
the data mining of blockchain data, yet show several real-world applications. Special attention was
paid to anomaly detection and fraud detection, which were identified as the most prolific applications
of applying data mining methods on blockchain data. The paper concludes with challenges for future
investigations of this research area.
Keywords: anomaly detection, blockchain, distributed ledger, data mining, machine learning
Published in DKUM: 16.06.2025; Views: 0; Downloads: 6
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7. The OpenScience Slovenia metadata datasetMladen Borovič, Marko Ferme, Janez Brezovnik, Sandi Majninger, Albin Bregant, Goran Hrovat, Milan Ojsteršek, 2020, other scientific articles Abstract: The OpenScience Slovenia metadata dataset contains metadata entries for Slovenian public domain academic documents which include undergraduate and postgraduate theses, research and professional articles, along with other academic document types. The data within the dataset was collected as a part of the establishment of the Slovenian Open-Access Infrastructure which defined a unified document collection process and cataloguing for universities in Slovenia within the infrastructure repositories. The data was collected from several already established but separate library systems in Slovenia and merged into a single metadata scheme using metadata deduplication and merging techniques. It consists of text and numerical fields, representing attributes that describe documents. These attributes include document titles, keywords, abstracts, typologies, authors, issue years and other identifiers such as URL and UDC. The potential of this dataset lies especially in text mining and text classification tasks and can also be used in development or benchmarking of content-based recommender systems on real-world data.
Keywords: metadata, real world data, text data, text mining, text identification, natural language processing
Published in DKUM: 22.05.2025; Views: 0; Downloads: 8
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8. Prepoznavanje rastlin in njihovih bolezni z mobilno aplikacijoRok Trunkelj, 2025, undergraduate thesis Abstract: Raziskava obravnava prepoznavanje izbranih rastlin in njihovih bolezni s pomočjo
mobilne aplikacije. Na kratko so predstavljena uporabljena orodja: Orange Data mining,
Android Studio, MS Visio, Figma, Flask, Nginx in Gunicorn. Arhitektura rešitve obsega
virtualno okolje v oblaku s strežniki za dostop do modelov za klasifikacijo rastlin in
njihovih bolezni in aplikacijo Android. Opisan je postopek izdelave modelov strojnega
učenja, ki so bili preneseni na strežnik. V nalogi so prikazani pomembni deli kode in
podana razlaga vseh aspektov delovanja aplikacije.
Keywords: umetna inteligenca, razvoj aplikacije, analiza podatkov, podatkovno
rudarjenje, Orange Data Mining.
Published in DKUM: 09.04.2025; Views: 0; Downloads: 17
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9. Towards circularity in Serbian mining: unlocking the potential of flotation tailings and fly ashNela Vujović, Vesna Alivojvodić, Dragana Radovanović, Marija Štulović, Miroslav Sokić, Filip Kokalj, 2025, original scientific article Abstract: This paper examines sustainable industrial practices in Serbia, particularly in the mining and energy sector, focusing on the potential of flotation tailings and fly ash, as materials with the largest share in disposed waste in Serbia in 2023 (95%). It highlights the environmental challenges of mining waste and explores innovative approaches to waste management within the circular economy framework. The study analyzes the current state of mining waste in Serbia, particularly in copper mining regions in the east of the country. It discusses the potential for metal recovery from waste and its reuse in various industries. The research also investigates the use of fly ash from thermal power plants as a valuable resource in the construction industry and other sectors. The paper reviews existing initiatives and legislation in Serbia in order to promote sustainable mining practices and waste utilization. By presenting case studies and potential applications, the study demonstrates how implementing circular economy principles in the mining sector can contribute to environmental protection, resource conservation, and economic growth in Serbia. The comprehensive overview of the current state in Serbia provides a solid foundation for establishing a higher degree of circularity in the mining and energy sectors.
Keywords: mining, flotation tailings, fly ash, Serbia, circular economy
Published in DKUM: 17.03.2025; Views: 0; Downloads: 6
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