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Entropy
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Advances in Data Mining and Coding Theory for Data Compression
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Advances in Data Mining and Coding Theory for Data Compression

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 980

Special Issue Editors

Dr. Krista Rizman Žalik

E-Mail Website
Guest Editor
1. Faculty of Natural Sciences and Mathematics, University of Maribor, SI-2000 Maribor, Slovenia
2. Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia
Interests: knowledge discovery; data mining; clustering; community detection; complex networks; data compression
Special Issues, Collections and Topics in MDPI journals
Dr. Štefan Kohek

E-Mail Website
Guest Editor
Faculty of electrical engineering and computer science, University of Maribor, SI-2000 Maribor, Slovenia
Interests: tree growth simulation; parallel computation; remote sensing; evolutionary computation; computer graphics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data mining is an important research field for revealing the structure of data, anomalies, rules, associations, clusters, and classes hidden within data sets, thereby making them understandable for further use. Data mining can be performed on structured, unstructured, and semi-structured data originating from natural, social, and artificial systems. The extracted knowledge can also be used in coding theory for more efficient data compression to encode information that requires less storage space than the original representation.

The aim of this Special Issue is to highlight the research topics of data mining and coding theory for data compression in all types of natural, artificial, social, and other complex systems. Researchers are encouraged to present the most recent developments in both theoretical and experimental studies aimed at better understanding different structured, unstructured, and semi-structured data for more efficient data compression.

Dr. Krista Rizman Žalik
Dr. Štefan Kohek
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. Entropy is an international peer-reviewed open access monthly 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 2600 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

  • big data
  • network data
  • data mining
  • learning
  • clustering
  • community detection
  • data compression
  • machine learning
  • information science
  • coding theory

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Published Papers (1 paper)

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Research

27 pages, 7663 KiB  
Article
Mining Suicidal Ideation in Chinese Social Media: A Dual-Channel Deep Learning Model with Information Gain Optimization
by Xiuyang Meng, Xiaohui Cui, Yue Zhang, Shiyi Wang, Chunling Wang, Mairui Li and Jingran Yang
Entropy 2025, 27(2), 116; https://doi.org/10.3390/e27020116 - 24 Jan 2025
Viewed by 634
Abstract
The timely identification of suicidal ideation on social media is pivotal for global suicide prevention efforts. Addressing the challenges posed by the unstructured nature of social media data, we present a novel Chinese-based dual-channel model, DSI-BTCNN, which leverages deep learning to discern patterns [...] Read more.
The timely identification of suicidal ideation on social media is pivotal for global suicide prevention efforts. Addressing the challenges posed by the unstructured nature of social media data, we present a novel Chinese-based dual-channel model, DSI-BTCNN, which leverages deep learning to discern patterns indicative of suicidal ideation. Our model is designed to process Chinese data and capture the nuances of text locality, context, and logical structure through a fine-grained text enhancement approach. It features a complex parallel architecture with multiple convolution kernels, operating on two distinct task channels to mine relevant features. We propose an information gain-based IDFN fusion mechanism. This approach efficiently allocates computational resources to the key features associated with suicide by assessing the change in entropy before and after feature partitioning. Evaluations on a customized dataset reveal that our method achieves an accuracy of 89.64%, a precision of 92.84%, an F1-score of 89.24%, and an AUC of 96.50%, surpassing TextCNN and BiLSTM models by an average of 4.66%, 12.85%, 3.08%, and 1.66%, respectively. Notably, our proposed model has an entropy value of 81.75, which represents a 17.53% increase compared to the original DSI-BTCNN model, indicating a more robust detection capability. This enhanced detection capability is vital for real-time social media monitoring, offering a promising tool for early intervention and potentially life-saving support. Full article
(This article belongs to the Special Issue Advances in Data Mining and Coding Theory for Data Compression)
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