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Advanced Data Mining and Applications

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

  • 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, Edmonton, University of Alberta, 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 8347)

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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Table of contents (47 papers)

  1. Front Matter

    Download chapter PDF

  2. Clustering

    1. Semi-supervised Clustering Ensemble Evolved by Genetic Algorithm for Web Video Categorization

      • Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang
      Pages 1-12

    2. A Scalable Approach for General Correlation Clustering

      • Yubo Wang, Linli Xu, Yucheng Chen, Hao Wang
      Pages 13-24

    3. A Fast Spectral Clustering Method Based on Growing Vector Quantization for Large Data Sets

      • Xiujun Wang, Xiao Zheng, Feng Qin, Baohua Zhao
      Pages 25-33

    4. A Novel Deterministic Sampling Technique to Speedup Clustering Algorithms

      • Sanguthevar Rajasekaran, Subrata Saha
      Pages 34-46

    5. Software Clustering Using Automated Feature Subset Selection

      • Zubair Shah, Rashid Naseem, Mehmet A. Orgun, Abdun Mahmood, Sara Shahzad
      Pages 47-58

    6. The Use of Transfer Algorithm for Clustering Categorical Data

      • Zhengrong Xiang, Lichuan Ji
      Pages 59-70

    7. eDARA: Ensembles DARA

      • Chung Seng Kheau, Rayner Alfred, HuiKeng Lau
      Pages 71-82

    8. Efficient Mining Maximal Variant and Low Usage Rate Biclusters without Candidate Maintenance in Real Function-Resource Matrix: The DeCluster Algorithm

      • Lihua Zhang, Miao Wang, Zhengjun Zhai, Guoqing Wang
      Pages 83-94

  3. Association Rule Mining

    1. MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining

      • Philippe Fournier-Viger, Espérance Mwamikazi, Ted Gueniche, Usef Faghihi
      Pages 95-106

  4. Pattern Mining

    1. Mining Frequent Patterns in Print Logs with Semantically Alternative Labels

      • Xin Li, Lei Zhang, Enhong Chen, Yu Zong, Guandong Xu
      Pages 107-119

    2. Minimising K-Dominating Set in Arbitrary Network Graphs

      • Guangyuan Wang, Hua Wang, Xiaohui Tao, Ji Zhang, Jinhua Zhang
      Pages 120-132

  5. Regression

    1. Logistic Regression Bias Correction for Large Scale Data with Rare Events

      • Zhen Qiu, Hongyan Li, Hanchen Su, Gaoyan Ou, Tengjiao Wang
      Pages 133-144

    2. An Automatical Moderating System for FML Using Hashing Regression

      • Peichao Zhang, Minyi Guo
      Pages 145-156

    3. Batch-to-Batch Iterative Learning Control Based on Kernel Independent Component Regression Model

      • Ganping Li, Jun Zhao, Fuyang Zhang, Zhizhen Ni
      Pages 157-164

  6. Prediction

    1. Deep Architecture for Traffic Flow Prediction

      • Wenhao Huang, Haikun Hong, Man Li, Weisong Hu, Guojie Song, Kunqing Xie
      Pages 165-176

    2. Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction

      • Ted Gueniche, Philippe Fournier-Viger, Vincent S. Tseng
      Pages 177-188

    3. Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias

      • Orlando P. Zacarias, Henrik Boström
      Pages 189-200

    4. Protein Interaction Hot Spots Prediction Using LS-SVM within the Bayesian Interpretation

      • Juhong Qi, Xiaolong Zhang, Bo Li
      Pages 201-210

    5. Predicting the Survival Status of Cancer Patients with Traditional Chinese Medicine Symptom Variation Using Logistic Regression Model

      • Min Wan, Liying Fang, Mingwei Yu, Wenshuai Cheng, Pu Wang
      Pages 211-218
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Other volumes

  1. Advanced Data Mining and Applications

  2. Advanced Data Mining and Applications

Keywords

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.

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, Edmonton, University of Alberta, 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 II

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

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-642-53917-6

  • Publisher: Springer Berlin, Heidelberg

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

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2013

  • Softcover ISBN: 978-3-642-53916-9Published: 07 January 2014

  • eBook ISBN: 978-3-642-53917-6Published: 16 December 2013

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXII, 538

  • Number of Illustrations: 163 b/w illustrations

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

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