Advances in Knowledge Discovery and Data Mining

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Advances in Knowledge Discovery and Data Mining

30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2026, Hong Kong, China, June 9-12, 2026, Proceedings, Part III

Educational equipment and technology, computer-aided learning (CAL) Electronics engineering Network hardware Mathematical theory of computation Maths for computer scientists Artificial intelligence Digital signal processing (DSP)

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Collection: Lecture Notes in Computer Science

Language: English

Published by: Springer

Published on: 8th June 2026

Format: LCP-protected ePub

ISBN: 9789819214655


Proceedings of the 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2026

The four-volume set, LNAI 16597-16600, constitutes the proceedings of the 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, held in Hong Kong, China, during June 9–12, 2026. The 184 full papers presented in this book were carefully selected and reviewed from 728 submissions.

Conference Tracks

This program featured three tracks, the Main Track, the Survey Track, and the Special Track on LLMs for Data Science.

Main Track

The Main Track continued its tradition of being the premier forum for the presentation of research results and experience reports on knowledge discovery, data science, and machine learning.

Survey Track

The Survey Track was introduced in 2025 for the first time, to promote the dissemination of insightful survey papers: survey papers are intended to provide a structured synthesis of a particular topic in the area of data mining, including but not limited to theoretical foundations of mining, inference, and learning, big data technologies, as well as security, privacy, and integrity, for the perusal of junior researchers and of experts from other research fields.

Special Track on LLMs

As the rapid advancements in Large Language Models (LLMs) have opened new avenues for innovation and research across various domains, particularly in the field of data science, we introduced the Special Track on LLMs, which aimed to explore the transformative potential of LLMs for data science, bringing together researchers, practitioners, and industry experts to discuss the latest developments, challenges, and opportunities in this rapidly growing area.

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