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Electronics
Special Issues
Data Retrieval and Data Mining
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Data Retrieval and Data Mining

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 25 June 2025 | Viewed by 2956

Special Issue Editors

Prof. Dr. Yangjun Chen

E-Mail Website
Guest Editor
Department of Applied Computer Science, University of Winnipeg, Winnipeg MB R3B 2E9, Canada
Interests: databases; algorithms; graph theory and combinatorics
Prof. Dr. Carson K. Leung

E-Mail Website
Guest Editor
Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: AI; data mining and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

While data retrieval focuses on finding existing data within a dataset as quickly as possible, data mining refers to a process of searching hidden information from a large number of data through algorithms. Both of them have undergone rapid development with the advances in information science, computer science, statistics, and mathematics. They are playing crucial roles in handling and understanding large datasets, and have already greatly impacted the research and development of modern science and technology in different ways, as well as people’s daily life.

The Special Issue “Data Retrieval and Data Mining” is planned to collect some most recent research work in these two fields, including information retrieval (by which we will quickly find relevant documents, records, graphs, or images), web scraping (by which we will extract data from websites, or data streams), data harvesting (by which we will gather specific data from various sources, either homogenous or heterogenous), as well as data selecting (by which we will choose and sieve relevant data), data cleaning (by which we will preprocess and clean data for certain purposes), data transformation (by which we will change data into a suitable format for further treatment) and data mining (by which we will evaluate patterns and derive values).

Prof. Dr. Yangjun Chen
Prof. Dr. Carson Leung
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data retrieval
  • data mining
  • Web
  • databases
  • data indexing
  • association rules
  • statistical analysis

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Published Papers (3 papers)

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Research

14 pages, 450 KiB  
Article
Consumer Transactions Simulation Through Generative Adversarial Networks Under Stock Constraints in Large-Scale Retail
by Sergiy Tkachuk, Szymon Łukasik and Anna Wróblewska
Electronics 2025, 14(2), 284; https://doi.org/10.3390/electronics14020284 - 12 Jan 2025
Viewed by 596
Abstract
In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative [...] Read more.
In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability—an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling. Full article
(This article belongs to the Special Issue Data Retrieval and Data Mining)
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25 pages, 4115 KiB  
Article
Comparison of Medical Opinions About the Decrease in Autopsies in Mexican Hospitals Using Data Mining
by Araceli Olmos-Vallejo, Lisbeth Rodríguez-Mazahua, José Antonio Palet-Guzmán, Isaac Machorro-Cano, Giner Alor-Hernández and Jair Cervantes
Electronics 2024, 13(23), 4686; https://doi.org/10.3390/electronics13234686 - 27 Nov 2024
Viewed by 592
Abstract
Subgroup discovery (SD) is a data mining technique that allows us to obtain the properties of each element given a particular population; these properties are of interest for a specific study, finding the most important or significant subgroups of the population. Also, the [...] Read more.
Subgroup discovery (SD) is a data mining technique that allows us to obtain the properties of each element given a particular population; these properties are of interest for a specific study, finding the most important or significant subgroups of the population. Also, the larger the population, the more successful the analysis and the creation of the subgroups, since, on this basis, the possibility of finding more unusual characteristics among the elements of the population is greater. The principal purpose of SD is not to obtain a predictive function, but to achieve a result that users can comprehend and interpret easily, and at the same time provide a more complete and suggestive description of the data. In this paper, we present an application of this technique to the medical field to analyze the opinions of physicians on the decreasing rates of autopsies in Mexican hospitals, utilizing five SD algorithms. The results obtained are the rules that allow for the comparison of medical opinions in three hospitals. Full article
(This article belongs to the Special Issue Data Retrieval and Data Mining)
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23 pages, 6186 KiB  
Article
A Comparative Analysis of Machine Learning Algorithms for Identifying Cultural and Technological Groups in Archaeological Datasets through Clustering Analysis of Homogeneous Data
by Maurizio Troiano, Eugenio Nobile, Flavia Grignaffini, Fabio Mangini, Marco Mastrogiuseppe, Cecilia Conati Barbaro and Fabrizio Frezza
Electronics 2024, 13(14), 2752; https://doi.org/10.3390/electronics13142752 - 13 Jul 2024
Cited by 3 | Viewed by 1327
Abstract
Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological [...] Read more.
Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions. Full article
(This article belongs to the Special Issue Data Retrieval and Data Mining)
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