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Data Mining

19th Australasian Conference on Data Mining, AusDM 2021, Brisbane, QLD, Australia, December 14-15, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Yue Xu

    1. Queensland University of Technology, Brisbane, Australia

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  2. Rosalind Wang

    1. Western Sydney University, Parramatta, Australia

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  3. Anton Lord

    1. University of Queensland, Herston, Australia

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  4. Yee Ling Boo

    1. RMIT University, Melbourne, Australia

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  5. Richi Nayak

    1. Queensland University of Technology, Brisbane, Australia

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  6. Yanchang Zhao

    1. Data61, CSIRO, Canberra, Australia

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  7. Graham Williams

    1. Australian National University, Canberra, Australia

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1504)

Included in the following conference series:

Conference proceedings info: AusDM 2021.

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xii
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  2. Research Track

    1. Front Matter

      Pages 1-1
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    2. Parallel Nonlinear Dimensionality Reduction Using GPU Acceleration

      • Yezihalem Tegegne, Zhonglin Qu, Yu Qian, Quang Vinh Nguyen
      Pages 3-15

    3. Taking the Confusion Out of Multinomial Confusion Matrices and Imbalanced Classes

      • David Lovell, Bridget McCarron, Brendan Langfield, Khoa Tran, Andrew P. Bradley
      Pages 16-30

    4. Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs

      • Taishi Nishiyama, Atsutoshi Kumagai, Akinori Fujino, Kazunori Kamiya
      Pages 31-46

    5. A Drift Aware Hierarchical Test Based Approach for Combating Social Spammers in Online Social Networks

      • Darshika Koggalahewa, Yue Xu, Ernest Foo
      Pages 47-61

    6. Hospital Readmission Prediction Using Semantic Relations Between Medical Codes

      • Sea Jung Im, Yue Xu, Jason Watson
      Pages 62-71

    7. HFM++: An Enhanced Holographic Factorization Machine for Recommendation

      • Zhengxin Fang, Mingcheng Qu, Shuai Zhang, Jingyu Zhang, Yi Yuan, Lina Yao et al.
      Pages 72-85

    8. Deep Learning for Bias Detection: From Inception to Deployment

      • Md Abul Bashar, Richi Nayak, Anjor Kothare, Vishal Sharma, Kesavan Kandadai
      Pages 86-101

    9. Exploring Fusion Strategies in Deep Learning Models for Multi-Modal Classification

      • Duoyi Zhang, Richi Nayak, Md Abul Bashar
      Pages 102-117

  3. Application Track

    1. Front Matter

      Pages 119-119
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    2. Chameleon: A Python Workflow Toolkit for Feature Selection

      • Diviya Thilakeswaran, Simon McManis, X. Rosalind Wang
      Pages 121-135

    3. PostMatch: A Framework for Efficient Address Matching

      • Darren Yates, Md Zahidul Islam, Yanchang Zhao, Richi Nayak, Vladimir Estivill-Castro, Salil Kanhere
      Pages 136-151

    4. Detection of Classical Cipher Types with Feature-Learning Approaches

      • Ernst Leierzopf, Vasily Mikhalev, Nils Kopal, Bernhard Esslinger, Harald Lampesberger, Eckehard Hermann
      Pages 152-164

    5. SOMPS-Net: Attention Based Social Graph Framework for Early Detection of Fake Health News

      • Prasannakumaran Dhanasekaran, Harish Srinivasan, S. Sowmiya Sree, I. Sri Gayathri Devi, Saikrishnan Sankar, Vineeth Vijayaraghavan
      Pages 165-179

    6. The Impact of Sentiment in the News Media on Daily and Monthly Stock Market Returns

      • Justin Case, Adam Clements
      Pages 180-195

    7. Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland

      • Yasuko Okamoto, Thirunavukarasu Balasubramaniam, Richi Nayak
      Pages 196-208

    8. A Semi-automatic Data Extraction System for Heterogeneous Data Sources: a Case Study from Cotton Industry

      • Richi Nayak, Thirunavukarasu Balasubramaniam, Sangeetha Kutty, Sachindra Banduthilaka, Erin Peterson
      Pages 209-222

    9. Nonnegative Matrix Factorization to Understand Spatio-Temporal Traffic Pattern Variations During COVID-19: A Case Study

      • Anandkumar Balasubramaniam, Thirunavukarasu Balasubramaniam, Rathinaraja Jeyaraj, Anand Paul, Richi Nayak
      Pages 223-234

  4. Correction to: Taking the Confusion Out of Multinomial Confusion Matrices and Imbalanced Classes

    • David Lovell, Bridget McCarron, Brendan Langfield, Khoa Tran, Andrew P. Bradley
    Pages C1-C1
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  1. Data Mining

Keywords

About this book

This book constitutes the refereed proceedings of the 19th Australasian Conference on Data Mining, AusDM 2021, held in Brisbane, Queensland, Australia, in December 2021.* 

The 16 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in sections on research track and application track. 

*Due to the COVID-19 pandemic the conference was held online. 


Editors and Affiliations

  • Queensland University of Technology, Brisbane, Australia

    Yue Xu, Richi Nayak

  • Western Sydney University, Parramatta, Australia

    Rosalind Wang

  • University of Queensland, Herston, Australia

    Anton Lord

  • RMIT University, Melbourne, Australia

    Yee Ling Boo

  • Data61, CSIRO, Canberra, Australia

    Yanchang Zhao

  • Australian National University, Canberra, Australia

    Graham Williams

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