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Advanced Data Mining and Applications

9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part I

  • Conference proceedings
  • © 2013

Overview

Editors:
  1. Hiroshi Motoda

    1. US Air Force Office of Scientific Research, Tokyo, Japan

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  2. Zhaohui Wu

    1. School of Computer Science and Technology, Zhejiang University, Hangzhou, China

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  3. Longbing Cao

    1. Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

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  4. Osmar Zaiane

    1. Department of Computing Science, University of Alberta, Edmonton, Canada

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  5. Min Yao

    1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

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  6. Wei Wang

    1. School of Computer Science, Fudan University, Shanghai, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8346)

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

Included in the following conference series:

Conference proceedings info: ADMA 2013.

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About this book

The two-volume set LNAI 8346 and 8347 constitutes the thoroughly refereed proceedings of the 9th International Conference on Advanced Data Mining and Applications, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 regular papers and 64 short papers presented in these two volumes were carefully reviewed and selected from 222 submissions. The papers included in these two volumes cover the following topics: opinion mining, behavior mining, data stream mining, sequential data mining, web mining, image mining, text mining, social network mining, classification, clustering, association rule mining, pattern mining, regression, predication, feature extraction, identification, privacy preservation, applications, and machine learning.

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Table of contents (49 papers)

  1. Front Matter

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  2. Opinion Mining

    1. Mining E-Commerce Feedback Comments for Dimension Rating Profiles

      • Lishan Cui, Xiuzhen Zhang, Yan Wang, Lifang Wu
      Pages 1-12

    2. Generating Domain-Specific Sentiment Lexicons for Opinion Mining

      • Zaher Salah, Frans Coenen, Davide Grossi
      Pages 13-24

    3. Effective Comment Sentence Recognition for Feature-Based Opinion Mining

      • Hui Song, Botian Yang, Xiaoqiang Liu
      Pages 25-35

    4. Exploiting Co-occurrence Opinion Words for Semi-supervised Sentiment Classification

      • Suke Li, Jinmei Hao, Yanbing Jiang, Qi Jing
      Pages 36-47

  3. Behavior Mining

    1. HN-Sim: A Structural Similarity Measure over Object-Behavior Networks

      • Jiazhen Nian, Shanshan Wang, Yan Zhang
      Pages 48-59

    2. Community Based User Behavior Analysis on Daily Mobile Internet Usage

      • Jamal Yousaf, Juanzi Li, Yuanchao Ma
      Pages 60-71

  4. Stream Mining

    1. Tracking Drift Types in Changing Data Streams

      • David T. J. Huang, Yun Sing Koh, Gillian Dobbie, Russel Pears
      Pages 72-83

    2. Continuously Extracting High-Quality Representative Set from Massive Data Streams

      • Xiaokang Ji, Xiuli Ma, Ting Huang, Shiwei Tang
      Pages 84-96

    3. Change Itemset Mining in Data Streams

      • Minmin Zhang, Gillian Dobbie, Yun Sing Koh
      Pages 97-108

  5. Sequential Data Mining

    1. TKS: Efficient Mining of Top-K Sequential Patterns

      • Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Espérance Mwamikazi, Rincy Thomas
      Pages 109-120

    2. When Optimization Is Just an Illusion

      • Muhammad Marwan Muhammad Fuad
      Pages 121-132

    3. Accurate and Fast Dynamic Time Warping

      • Hailin Li, Libin Yang
      Pages 133-144

    4. Online Detecting Spreading Events with the Spatio-temporal Relationship in Water Distribution Networks

      • Ting Huang, Xiuli Ma, Xiaokang Ji, Shiwei Tang
      Pages 145-156

    5. MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

      • Michal Å ebek, Martin Hlosta, Jaroslav Zendulka, Tomáš HruÅ¡ka
      Pages 157-168

    6. Mining Maximal Sequential Patterns without Candidate Maintenance

      • Philippe Fournier-Viger, Cheng-Wei Wu, Vincent S. Tseng
      Pages 169-180

  6. Web Mining

    1. Improved Slope One Collaborative Filtering Predictor Using Fuzzy Clustering

      • Tianyi Liang, Jiancong Fan, Jianli Zhao, Yongquan Liang, Yujun Li
      Pages 181-192

    2. Towards Building Virtual Vocabularies in the Semantic Web

      • Yunqing Wen, Xiang Zhang, Kai Shen, Peng Wang
      Pages 193-204

    3. Web Mining Accelerated with In-Memory and Column Store Technology

      • Patrick Hennig, Philipp Berger, Christoph Meinel
      Pages 205-216

  7. Image Mining

    1. Constructing a Novel Pos-neg Manifold for Global-Based Image Classification

      • Rong Zhu, Jianhua Yang, Yonggang Li, Jie Xu
      Pages 217-228
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Other volumes

  1. Advanced Data Mining and Applications

  2. Advanced Data Mining and Applications

Editors and Affiliations

  • US Air Force Office of Scientific Research, Tokyo, Japan

    Hiroshi Motoda

  • School of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Zhaohui Wu

  • Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

    Longbing Cao

  • Department of Computing Science, University of Alberta, Edmonton, Canada

    Osmar Zaiane

  • College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Min Yao

  • School of Computer Science, Fudan University, Shanghai, China

    Wei Wang

Bibliographic Information

  • Book Title: Advanced Data Mining and Applications

  • Book Subtitle: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part I

  • Editors: Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-642-53914-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2013

  • Softcover ISBN: 978-3-642-53913-8Published: 07 January 2014

  • eBook ISBN: 978-3-642-53914-5Published: 14 December 2013

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXII, 588

  • Number of Illustrations: 217 b/w illustrations

  • Topics: Artificial Intelligence, Data Mining and Knowledge Discovery, Information Storage and Retrieval

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