Data mining is the computational process of discovering valuable knowledge from data and has been widely recognized as the cornerstone of Data Science. It plays a key role in important applications in science, engineering, healthcare, business, and medicine. Typical datasets in these fields are usually massive, complex, and noisy. Extracting knowledge from these datasets requires sophisticated, high-performance, and principled analysis techniques and algorithms, with sound theoretical and statistical foundations. These techniques, in turn, require implementation on computational infrastructures optimized for high performance. Powerful visualization technologies and comprehensive user interfaces are also essential for data mining tools appealing to researchers, analysts, data scientists, and application developers from different disciplines.
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the premier and leading international conferences for data mining. The 28th edition of PAKDD will be held in Taipei, Taiwan, from May 7–10, 2024. PAKDD is a prestigious and highly selective conference that includes invited talks as well as refereed full research papers. It provides an international peer-reviewed forum for researchers and industry practitioners who are addressing the problems to share their new ideas, original research results, and practical development experiences from all KDD-related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and emerging applications. Everyone new to the field has many opportunities to learn about cutting-edge research by attending visionary keynote speeches, paper presentations, tutorials, and workshops.
This year, the dominant topics include data cleaning and preparation, data transformation, mining, inference, learning, explainability, data privacy, dissemination of results, theoretical foundations for novel models and algorithms for data science problems in science, business, medicine, and engineering, along with an emphasis on practical yet principled novel models of search and data mining, algorithm design and analysis, economic implications, and in-depth experimental analysis of accuracy and performance. We also invite paper submissions at the intersection of data science and society as part of the research track.
