Abstract
Cross-domain data mining has gained prominence as a crucial research area in machine learning and data mining, focusing on leveraging knowledge acquired in one domain to improve performance in another. This paper explores the concepts of knowledge transfer and adaptation in the context of cross-domain data mining. We present an overview of the challenges, methodologies, and applications related to transferring and adapting knowledge across diverse domains. The paper also includes illustrative tables to enhance understanding and provide a comprehensive reference for researchers and practitioners.
Keywords- Cross-Domain Data Mining, Knowledge Transfer, Adaptation, Machine Learning, Data Mining, Transfer Learning, Domain Adaptation, Domain Discrepancy, Labeling Heterogeneity, Distribution Shift, Feature Variability.
Full Issue
| View or download the full issue | PDF 111-123 |