Application of Machine Learning in Data Mining

Dear Colleagues,

The field of data mining has become increasingly important in recent years due to the proliferation of digital data generated from various sources such as social media, sensors, and the Internet of Things. Data mining refers to the process of discovering patterns, correlations, and insights within large datasets using various techniques such as statistical analysis, machine learning, and artificial intelligence. Machine learning is a subfield of artificial intelligence that has gained popularity in recent years due to its ability to learn from data and make predictions or decisions without being explicitly programmed. Machine learning algorithms can be used to automate the process of data mining and make it more efficient and accurate. By analyzing large datasets, machine learning algorithms can reveal hidden patterns and relationships and make predictions or decisions based on the information gleaned.

The combination of machine learning and data mining has resulted in significant advances in various fields such as healthcare, finance, and engineering. The importance of this research area lies in the potential of machine learning to transform data mining from a manual and time-consuming process to an automated and efficient one. Machine learning algorithms can handle large amounts of data and discover patterns that might not be immediately apparent to humans. Additionally, machine learning can improve the accuracy of data mining results by reducing the risk of human error.

The aim of this Special Issue is to bring together researchers and practitioners who are interested in the application of machine learning techniques in data mining. The Special Issue will provide a platform for the exchange of ideas and the sharing of new research results in this area. This Special Issue falls within the scope of MDPI Electronics, which publishes research on electronic engineering, computer science, and related fields. The subject of machine learning in data mining fits well within the journal scope as it involves the application of advanced computing techniques to electronic data.

For this Special Issue, we welcome original research articles and reviews . We will accept submissions concerning a wide range of topics related to the application of machine learning in data mining. Some potential themes that could be explored include the following:

• Techniques for data preprocessing and cleaning;
• Novel algorithms and models for data mining;
• Applications of machine learning in specific fields (e.g., healthcare, finance, energy, engineering, IoT, biomedical informatics, scientific machine learning, chemistry, computer vision, NLP, and multimedia);
• Performance evaluation and benchmarking of machine learning algorithms;
• Interpretability and explainability of machine learning models in data mining;
• Big data analytics and machine learning for large-scale data mining;
• Privacy-preserving data mining using machine learning techniques;
• Integration of machine learning with other data analysis techniques (e.g., graph mining and text mining).

These themes are just suggestions; other relevant topics will also be considered. To conclude, this Special Issue aims to showcase the latest research and developments in the application of machine learning in data mining and to highlight the potential impact of this area on various fields.

Dr. Jin Lu
Guest Editor

Manuscript Submission Information

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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.

• machine learning
• data mining
• applications
• engineering
• health
• bioinformatics
• IoT
• NLP