Trends and Applications in Knowledge Discovery and Data Mining

1. Front Matter

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2. Data Mining Applications in Industry and Government

   1. Using Scan-Statistical Correlations for Network Change Analysis

      • Adriel Cheng, Peter Dickinson

      Pages 1-13

   2. Predicting High Impact Academic Papers Using Citation Network Features

      • Daniel McNamara, Paul Wong, Peter Christen, Kee Siong Ng

      Pages 14-25

   3. An OLAP Server for Sensor Networks Using Augmented Statistics Trees

      • Neil Dunstan

      Pages 26-35

   4. Indirect Information Linkage for OSINT through Authorship Analysis of Aliases

      • Robert Layton, Charles Perez, Babiga Birregah, Paul Watters, Marc Lemercier

      Pages 36-46

   5. Dynamic Similarity-Aware Inverted Indexing for Real-Time Entity Resolution

      • Banda Ramadan, Peter Christen, Huizhi Liang, Ross W. Gayler, David Hawking

      Pages 47-58

   6. Identifying Dominant Economic Sectors and Stock Markets: A Social Network Mining Approach

      • Ram Babu Roy, Uttam Kumar Sarkar

      Pages 59-70

   7. Ensemble Learning Model for Petroleum Reservoir Characterization: A Case of Feed-Forward Back-Propagation Neural Networks

      • Fatai Anifowose, Jane Labadin, Abdulazeez Abdulraheem

      Pages 71-82

   8. Visual Data Mining Methods for Kernel Smoothed Estimates of Cox Processes

      • David Rohde, Ruth Huang, Jonathan Corcoran, Gentry White

      Pages 83-94

   9. Real-Time Television ROI Tracking Using Mirrored Experimental Designs

      • Brendan Kitts, Dyng Au, Brian Burdick

      Pages 95-108

   10. On the Evaluation of the Homogeneous Ensembles with CV-Passports

       • Vladimir Nikulin, Aneesha Bakharia, Tian-Hsiang Huang

       Pages 109-120

   11. Parallel Sentiment Polarity Classification Method with Substring Feature Reduction

       • Yaowen Zhang, Xiaojun Xiang, Cunyan Yin, Lin Shang

       Pages 121-132

   12. Identifying Authoritative and Reliable Contents in Community Question Answering with Domain Knowledge

       • Lifan Guo, Xiaohua Hu

       Pages 133-142

3. Data Analytics for Targeted Healthcare

   1. On the Application of Multi-class Classification in Physical Therapy Recommendation

      • Jing Zhang, Douglas Gross, Osmar R. Zaïane

      Pages 143-154

   2. EEG-MINE: Mining and Understanding Epilepsy Data

      • SunHee Kim, Christos Faloutsos, Hyung-Jeong Yang

      Pages 155-167

   3. A Constraint and Rule in an Enhancement of Binary Particle Swarm Optimization to Select Informative Genes for Cancer Classification

      • Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifumi Yoshioka

      Pages 168-178

   4. Parameter Estimation Using Improved Differential Evolution (IDE) and Bacterial Foraging Algorithm to Model Tyrosine Production in Mus Musculus (Mouse)

      • Jia Xing Yeoh, Chuii Khim Chong, Yee Wen Choon, Lian En Chai, Safaai Deris, Rosli Md. Illias et al.

      Pages 179-190

   5. Threonine Biosynthesis Pathway Simulation Using IBMDE with Parameter Estimation

      • Chuii Khim Chong, Mohd Saberi Mohamad, Safaai Deris, Mohd Shahir Shamsir, Yee Wen Choon, Lian En Chai

      Pages 191-200

   6. A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network

      • Xinyu Wang, Chunhong Zhang, Yang Ji, Li Sun, Leijia Wu, Zhana Bao

      Pages 201-213

   7. Modelling Gene Networks by a Dynamic Bayesian Network-Based Model with Time Lag Estimation

      • Lian En Chai, Mohd Saberi Mohamad, Safaai Deris, Chuii Khim Chong, Yee Wen Choon

      Pages 214-222

This book constitutes the refereed proceedings at PAKDD Workshops 2013, affiliated with the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) held in Gold Coast, Australia in April 2013.
The 47 revised full papers presented were carefully reviewed and selected from 92 submissions. The workshops affiliated with PAKDD 2013 include: Data Mining Applications in Industry and Government (DMApps), Data Analytics for Targeted Healthcare (DANTH), Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE), Biologically Inspired Techniques for Data Mining (BDM), Constraint Discovery and Application (CDA), Cloud Service Discovery (CloudSD).

• association rule mining
• clustering
• health informatics
• machine learning
• social networks

• School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

  Jiuyong Li

• Advanced Analytics Institute, University of Technology, Sydney, Australia

  Longbing Cao, Can Wang

• Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

  Kay Chen Tan

• School of Automation, Guangdong University of Technology, Guangzhou, China

  Bo Liu

• School of Computing Science, Simon Fraser University, Burnaby, Canada

  Jian Pei

• Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

  Vincent S. Tseng

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