LEADER 03988nam 22007935 450 001 9911010533003321 005 20250617131705.0 010 $a981-9681-86-3 024 7 $a10.1007/978-981-96-8186-0 035 $a(CKB)39331674400041 035 $a(MiAaPQ)EBC32162249 035 $a(Au-PeEL)EBL32162249 035 $a(DE-He213)978-981-96-8186-0 035 $a(OCoLC)1525621702 035 $a(EXLCZ)9939331674400041 100 $a20250617d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Knowledge Discovery and Data Mining $e29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10?13, 2025, Proceedings, Part V /$fedited by Xintao Wu, Myra Spiliopoulou, Can Wang, Vipin Kumar, Longbing Cao, Yanqiu Wu, Yu Yao, Zhangkai Wu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (576 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15874 311 08$a981-9681-85-5 330 $aThe five-volume set, LNAI 158710 - 15874 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10?13, 2025. The conference received a total of 557 submissions to the main track, 35 submissions to the survey track and 104 submittion to the special track on LLMs. Of these, 134 papers have been accepted for the main track, 10 for the survey track and 24 for the LLM track. 68 papers have been transferred to the4 DSFA special session. The papers have been organized in topical sections as follows: Part I: Anomaly Detection; Business Data Analysis; Clustering; Continual Learning; Contrastive Learning; Data Processing for Learning; Part II: Fairness and Interpretability; Federated Learning; Graph Mining and GNN; Learning on Scientific Data; Part III: Machine Learning; Multi-modality; OOD and Optimization; Recommender Systems; Representation Learning and Generative AI; Part IV: Security and Privacy; Temporal Learning; Survey; Part V: LLM Fine-tuning and Prompt Engineering; Fairness and Interpretability of LLMs; LLM Application; OOD and Optimization of LLMs. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15874 606 $aArtificial intelligence 606 $aAlgorithms 606 $aEducation$xData processing 606 $aComputer science$xMathematics 606 $aSignal processing 606 $aComputer networks 606 $aArtificial Intelligence 606 $aDesign and Analysis of Algorithms 606 $aComputers and Education 606 $aMathematics of Computing 606 $aSignal, Speech and Image Processing 606 $aComputer Communication Networks 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aEducation$xData processing. 615 0$aComputer science$xMathematics. 615 0$aSignal processing. 615 0$aComputer networks. 615 14$aArtificial Intelligence. 615 24$aDesign and Analysis of Algorithms. 615 24$aComputers and Education. 615 24$aMathematics of Computing. 615 24$aSignal, Speech and Image Processing. 615 24$aComputer Communication Networks. 676 $a006.3 700 $aWu$b Xintao$01828597 701 $aSpiliopoulou$b Myra$01756207 701 $aWang$b Can$01828598 701 $aKumar$b Vipin$065762 701 $aCao$b Longbing$0921499 701 $aWu$b Yanqiu$01828599 701 $aYao$b Yu$01828600 701 $aWu$b Zhangkai$01828601 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911010533003321 996 $aAdvances in Knowledge Discovery and Data Mining$94397504 997 $aUNINA