Advances in Knowledge Discovery and Data Mining

20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part I

  • James Bailey
  • Latifur Khan
  • Takashi Washio
  • Gill Dobbie
  • Joshua Zhexue Huang
  • Ruili Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9651)

Table of contents

  1. Front Matter
    Pages I-XXIV
  2. Classification

    1. Front Matter
      Pages 1-1
    2. Yongxin Liao, Shenxi Yuan, Jian Chen, Qingyao Wu, Bin Li
      Pages 3-13
    3. Yang Lu, Yiu-ming Cheung, Yuan Yan Tang
      Pages 14-26
    4. Khanh Nguyen, Trung Le, Vu Nguyen, Dinh Phung
      Pages 27-39
    5. Qinzhe Zhang, Qin Zhang, Guodong Long, Peng Zhang, Chengqi Zhang
      Pages 40-51
    6. Yuxun Zhou, Jae Yeon Baek, Dan Li, Costas J. Spanos
      Pages 52-64
    7. Man Yu, Zongxia Xie, Hong Shi, Qinghua Hu
      Pages 65-76
    8. Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström
      Pages 77-88
    9. Agoritsa Polyzou, George Karypis
      Pages 89-101
    10. Tinu Theckel Joy, Santu Rana, Sunil Kumar Gupta, Svetha Venkatesh
      Pages 102-114
    11. Jianping Cao, Senzhang Wang, Fengcai Qiao, Hui Wang, Feiyue Wang, Philip S. Yu
      Pages 127-138
    12. Mahtab J. Fard, Sanjay Chawla, Chandan K. Reddy
      Pages 139-151
    13. Cheng Li, Sunil Gupta, Santu Rana, Wei Luo, Svetha Venkatesh, David Ashely et al.
      Pages 152-164
    14. Hsun-Ping Hsieh, Rui Yan, Cheng-Te Li
      Pages 177-188
  3. Feature Extraction and Pattern Mining

    1. Front Matter
      Pages 189-189
    2. Wei Wu, Bin Li, Ling Chen, Chengqi Zhang
      Pages 203-214

About these proceedings

Introduction

This two-volume set, LNAI 9651 and 9652, constitutes the thoroughly refereed proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, held in Auckland, New Zealand, in April 2016.

The 91 full papers were carefully reviewed and selected from 307 submissions. They are organized in topical sections named: classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.

Keywords

cloud-based high performance data mining cyber-security distributed data mining healthcare ubiquitous knowledge discovery agent-based data mining anomaly detection bioinformatics data mining data warehousing human factors interactive mining intrusion detection marketing OLAP opinion mining parallel data mining security social factors statistical methods

Editors and affiliations

  • James Bailey
    • 1
  • Latifur Khan
    • 2
  • Takashi Washio
    • 3
  • Gill Dobbie
    • 4
  • Joshua Zhexue Huang
    • 5
  • Ruili Wang
    • 6
  1. 1.The University of MelbourneMelbourneAustralia
  2. 2.The University of Texas at DallasRichardsonUSA
  3. 3.Osaka UniversityOsakaJapan
  4. 4.University of AucklandAucklandNew Zealand
  5. 5.Shenzhen UniversityShenzhenChina
  6. 6.Massey UniversityAucklandNew Zealand

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-31753-3
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-31752-6
  • Online ISBN 978-3-319-31753-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349