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Advances in Knowledge Discovery and Data Mining

21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part II

  • Conference proceedings
  • © 2017

Overview

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10235)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: PAKDD 2017.

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About this book

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.

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Keywords

Table of contents (65 papers)

  1. Clustering and Anomaly Detection

  2. Recommender Systems

  3. Feature Selection

Editors and Affiliations

  • Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim, Yang-Sae Moon

  • Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  • University of Technology Sydney, Sydney, Australia

    Longbing Cao

  • KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  • University of New South Wales, Sydney, Australia

    Xuemin Lin

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