8:30 - 9:15

Registration

9:15 - 9:30

AusDM 2014 Opening

9:30-10:30

Session 1: Clustering

• Automatic Detection of Cluster Structure Changes using Relative Density Self-Organizing Maps
  Denny, Pandu Wicaksono and Ruli Manurung
• Market Segmentation of EFTPOS Retailers
  Ashishkumar Singh, Grace Rumantir and Annie South
• Hartigan's Method for K-modes Clustering and Its Advantages
  Zheng Rong Xiang and Zahidul Islam

10:30 - 11:00

Coffee Break

11:00 - 12:20

Session 2: Linkage, Fusion & Cleaning

• Tree Based Scalable Indexing for Multi-Party Privacy Preserving Record Linkage
  Thilina Ranbaduge, Peter Christen and Dinusha Vatsalan
• Detecting Digital Newspaper Duplicates with Focus on eliminating OCR errors
  Yeshey Peden and Richi Nayak
• The Schema Last Approach to Data Fusion
  Neil Brittliff and Dharmendra Sharma
• A Triple Store Implementation to support Tabular Data
  Neil Brittliff and Dharmendra Sharma

12:20-1:30

Lunch

1:30 - 3:10

Session 3: Machine Learning

• Factors Influencing Robustness and Effectiveness of Conditional Random Fields in Active Learning Frameworks 
  Mahnoosh Kholghi, Laurianne Sitbon, Guido Zuccon and Anthony Nguyen
• Dynamic Class Prediction with Classifier Based Distance Measure
  Senay Yasar Saglam and Nick Street
• Improving Scalability and Performance of Random Forest Based Learning-to-Rank Algorithms by Aggressive Subsampling
  Muhammad Ibrahim and Mark Carman
• Pruned Annular Extreme Learning Machine Optimization based on RANSAC Multi Model Response Regularization
  Lavneet Singh and Girija Chetty
• Identifying Product Families Using Data Mining Techniques in Manufacturing Paradigm
  Israt Jahan Chowdhury and Richi Nayak

Workshop: R and Data Mining – Part 1

Dr. Yanchang Zhao
RDataMining.com

3:10-3:40

Coffee Break

3:40 - 4:40

Session 4: Health & Bioinformatics

• Evolving Wavelet Neural Networks for Breast Cancer Classification 
  Maryam Khan, Stephan Chalup and Alexandre Mendes
• Comparison of athletic performances across disciplines anisability classes
  Chris Barnes
• Locality-Sensitive Hashing for Protein Classification
  Lawrence Buckingham, James Hogan, Shlomo Geva and Wayne Kelly

Workshop: R and Data Mining – Part 2

Dr. Yanchang Zhao
RDataMining.com

6:45 – 10:00

Conference Banquet - Kookaburra River Queens
Eagle Street Pier

9:00 - 9:30

Registration

9:30-10:30

Keynote 1 - Learning in sequential decision problems

Prof Peter Bartlett

University of California, Berkeley, USA

10:30 - 11:00

Coffee Break

11:00 - 12:20

Session 5: Data Mining for Information Retrieval

• A Case Study of Utilising Concept Knowledge in a Topic Specific Document Collection
  Gavin Shaw and Richi Nayak
• Decreasing Uncertainty for Improvement of Relevancy Prediction  
  Libiao Zhang, Yuefeng Li and Moch Arif Bijaksana
• Pattern-based Topic Modelling for Query Expansion
  Yang Gao, Yue Xu and Yuefeng Li
• Content Based Image Retrieval Using Signature Representation 
  Dinesha Chathurani Nanayakkara Wasam Uluwitige, Shlomo Geva, Vinod Chandran and Timothy Chappell

12:20-1:30

Lunch

1:30 - 2:30

Keynote 2 - Making Sense of a Random World through Statistics

Prof Geoff McLachlan

University of Queensland, Brisbane, Australia

2:30 - 3:10

Session 6: Sensor Networks

• Towards Social Media as a Data Source for Opportunistic Sensor Networking
  James Meneghello, Kevin Lee and Nik Thompson
• Data Cleansing during Data Collection from Wireless Sensor Networks
  Md Zahidul Islam, Quazi Mamun and Md Geaur Rahman

3:10-3:40

Coffee Break

3:40 - 4:40

Session 7: Collaborative filtering & Recommendation

• An Efficient Tagging Data Interpretation and Representation Scheme for Item Recommendation
  Noor Ifada and Richi Nayak
• A Multidimensional Collaborative Filtering Fusion Approach with Dimensionality Reduction
  Xiaoyu Tang, Yue Xu, Ahmad Abdel-Hafez and Shlomo Geva
• Real-time Collaborative Filtering Recommender Systems
  Huizhi Liang, Haoran Du and Qing Wang

4:40 - 5:00

Conference close + best paper awards