Advances in Knowledge Discovery and Data Mining 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Kim, Jinho (Επιμελητής έκδοσης), Shim, Kyuseok (Επιμελητής έκδοσης), Cao, Longbing (Επιμελητής έκδοσης), Lee, Jae-Gil (Επιμελητής έκδοσης), Lin, Xuemin (Επιμελητής έκδοσης), Moon, Yang-Sae (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Lecture Notes in Computer Science, 10234
• Θέματα: Computer science.; Computer security.; Database management.; Data mining.; Information storage and retrieval.; Artificial intelligence.; Computer Science.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).; Information Storage and Retrieval.; Information Systems Applications (incl. Internet).; Database Management.; Systems and Data Security.

 This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction. 

 Classification and deep learning -- Social network and graph mining -- Privacy-preserving mining and security/risk applications -- Spatio-temporal and sequential data mining -- Clustering and anomaly detection -- Recommender system -- Feature selection -- Text and opinion mining -- Clustering and matrix factorization -- Dynamic, stream data mining -- Novel models and algorithms -- Behavioral data mining -- Graph clustering and community detection -- Dimensionality reduction.