Advances in Knowledge Discovery and Data Mining (eBook)

30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2026, Hong Kong, China, June 9-12, 2026, Proceedings, Part I
Artikelnummer: 978-981-9213-00-9
Einband: PDF
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The four-volume set, LNAI 16597-16600 constitutes the proceedings of the 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2026, 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.

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

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.

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.

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.

The four-volume set, LNAI 16597-16600 constitutes the proceedings of the 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2026, 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.

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

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.

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.

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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VerlagSpringer Nature Singapore
EinbandPDF
Erscheinungsjahr2026
Seitenangabe615 S.
AusgabekennzeichenEnglisch
AbbildungenXLIII, 615 p. 162 illus., 152 illus. in color.
Masse73'286 KB
PlattformPDF
ReiheLecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Computer Science; Computer Science ; Springer Nature Proceedings Computer Science
AutorWong, Raymond Chi-Wing (Hrsg.) / Tong, Hanghang (Hrsg.) / Lu, Hua (Hrsg.) / Kwok, James (Hrsg.) / Salim, Flora (Hrsg.) / Song, Yuanfeng (Hrsg.) / Yiu, Man Lung (Hrsg.)

Alle Bände der Reihe "Lecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Computer Science; Computer Science (R0); Springer Nature Proceedings Computer Science"

Weitere Titel von Raymond Chi-Wing (Hrsg.) Wong

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