Advances in Data Mining: Methods and Applications

Dear Colleagues,

Data mining has long been a cornerstone technique for extracting potentially useful patterns, associations, and knowledge from vast and complex datasets, playing an integral part in knowledge discovery. In an era characterized by explosive growth in data volume, velocity, and variety, from structured databases to unstructured text, images, sensor streams, and time-series data, advanced data mining methods remain essential for turning raw data into actionable insights across numerous domains.

While data mining has proven particularly valuable in educational contexts (e.g., learning analytics and educational data mining in e-learning platforms, student performance prediction, and personalized adaptive systems), its applications extend far beyond education. Fields including medicine (e.g., disease diagnosis from electronic health records and medical imaging), biology (e.g., genomic sequence analysis), finance (e.g., fraud detection and market trend forecasting), industry (e.g., predictive maintenance and supply chain optimization) generate or capture massive amounts of data, allowing sophisticated data mining techniques to uncover hidden relationships, detect anomalies, support decision-making, and drive innovation.

This Special Issue aims to showcase the latest advances in data mining algorithms, methodologies, and real-world applications. We invite researchers to submit high-quality original research articles, as well as comprehensive review papers, that address novel developments, theoretical improvements, or practical implementations in this dynamic field.

Topics of interest include, but are not limited to, the following:

• Novel algorithms and out-of-the-box approaches in data mining (clustering, classification, association rule mining, anomaly/outlier detection, sequential pattern mining, etc.).
• Integration of data mining with machine learning, deep learning, and explainable AI.
• Big data mining and scalable algorithms for large-scale datasets.
• Time-series data mining and forecasting.
• Text mining, web mining, and social media analytics.
• Graph and network mining—graph-based neural networks in data mining.
• Data mining in education and e-learning (learning analytics, educational data mining, adaptive systems, early dropout prediction).
• Data mining applications in healthcare and biomedicine (bioinformatics, medical imaging analysis).
• Data mining in biology and genomics.
• Data mining for industry, IoT, and sensor data.
• Data mining in cybersecurity.
• Data mining in decentralized autonomous organizations (DAOs).
• Case studies and real-world applications and impacts of data mining.

Dr. Javier Bravo-Agapito
Dr. Iván García-Magariño
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 250 words) can be sent to the Editorial Office for assessment.

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.

• data mining
• machine learning
• knowledge discovery
• deep learning
• learning analytics
• anomaly detection
• text mining
• time-series analysis
• graph mining
• explainable artificial intelligence