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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: 10 December 2024 | Viewed by 1116

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 (1 paper)

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Research

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 1 | Viewed by 870
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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