Data mining and knowledge discovery research is a dynamic field focused on extracting valuable insights and patterns from large datasets, bridging the gap between raw data and actionable knowledge. This category falls under Information and Computing Sciences, specifically within data management and data science, reflecting its pivotal role in handling complex information. Researchers and students benefit from JoVE Visualize, which enriches traditional PubMed articles by pairing them with JoVE’s experiment videos, offering a more immersive understanding of research methods and discoveries in this field.
Established methods in data mining and knowledge discovery include classification, clustering, association rule mining, and anomaly detection. These techniques enable researchers to organize, interpret, and summarize vast amounts of data effectively. Algorithms such as decision trees, support vector machines, and neural networks represent foundational tools. Preprocessing steps like data cleaning and dimensionality reduction are also central to enhancing data quality and analysis outcomes. These core practices have consistently driven advances in fields ranging from bioinformatics to market analysis.
Emerging methods in this field emphasize deep learning integration, automated machine learning (AutoML), and explainable AI to improve the interpretability and efficiency of data mining results. Innovative trends also focus on leveraging big data platforms and cloud computing to handle increasingly large and complex datasets. Additionally, real-time data mining and adaptive learning models are gaining traction, addressing the need for timely and dynamic knowledge discovery. Researchers interested in the Data mining and knowledge discovery impact factor often explore these developments to understand evolving publication landscapes. JoVE Visualize supports these insights by linking new research with experiment videos that illuminate novel methodologies.
Haitao Wang, Houqin Wu, Jia Tian, Lang Liu, Mengbo Zhu, Kaiqian Shu, Haijun Tang, Min Liu, Longhua Xu
Isla S Mackenzie, LaPrincess C Brewer, Alex Zhavoronkov
Adrian Huerta, Roberto Serrano-Notivoli, Stefan Brönnimann
Luo-Jia Ma, Yue-Ying Wang, Ming-Shuo Sun, Chun-Hui Zhang, Hua-Jian Ding, Xing-Yu Zhou, Qin Wang
Wei Zhang, Nana Yu, Xiangxiang Ji, Bocheng Lu, Xiaoyu Hui, Xiaolei Wang, Sixing Xi
Omer Dilian, Nadav Davidovitch, Karel Martens
Cailbhe Doherty, Maximus Baldwin, Rory Lambe, Marco Altini, Brian Caulfield