Recent Advances in Data Mining

Dear Colleagues,

Data mining is the procedure of identifying valid, potentially suitable, and understandable information; detecting patterns; building knowledge graphs; and finding anomalies and relationships in big data with Artificial-Intelligence-enabled IoT （AIoT). This process is essential for advancing knowledge in various fields dealing with raw data from web, text, numeric, media, or financial transactions. Its scope has expanded through hybridizing various data mining algorithms for use in financial technology and cryptocurrency, the blockchain, data sciences, sentiment analysis, and recommender systems. Moreover, data mining provides advantages in many practical fields, such as in preserving the privacy of health data analysis and mining, biology, data security, smart cities, and smart grids. It is also necessary to investigate the recent advances in data mining involving the incorporation of machine learning algorithms and artificial neural networks. Among other fields of artificial intelligence, machine and deep learning are certainly some of the most studied in recent years. There has been a massive shift in the last few decades due to the advent of deep learning, which has opened up unprecedented theoretic and application-based opportunities for data mining. This Topic will present a collection of articles reflecting the latest developments in data mining and related fields, investigating both practical and theoretical applications; knowledge discovery and extraction; image analysis; classification and clustering; FinTech and cryptocurrency; the blockchain and data security; privacy-preserving data mining; and many others. Contributions focused on both theoretical and practical models are welcome. Papers will be selected for inclusion based on their formal and technical soundness, experimental support, and relevance.

Prof. Dr. Qingshan Jiang
Dr. John (Junhu) Wang
Dr. Min Yang
Topic Editors