AusDM 2011 Program [pdf]

DAY1 Registration 8.30-9.20am Welcome and Keynote 1 9.20-10.30 Associate Professor Christopher W. Clifton, Purdue University: Privacy-Preserving Data Mining at 10 - What's Next? Morning tea 10.30-11.00 Paper presentation Session 1A and 1B 11.00-11.25 11.25-11.50 11.50-12.15 Lunch 12.15 - 1.30pm Keynote 2 1.30-2.30pm Professor Hussein Abbass, University of New South Wales: Mining Big Data Streams - The Fallacy of Blind Correlation and the Importance of Models Afternoon tea 2.30-3.00pm Paper presentation Session 2 3.00-3.25 3.25-3.50 3.50-4.15 4.15 Car shuttle to hotels Conference Dinner 7.30pm-10pm

DAY 2 Keynote 3 9.30-10.30 Dr Musa Mammadov, University of Ballarat, National ICT Australia: Drug-drug interactions - A Data Mining Approach Morning tea 10.30-11.00 Paper session 3A and 3B Paper presentation Session 3A and 3B 11.00-11.25 11.25-11.50 11.50-12.15 Lunch 12.15 - 1.30pm Paper presentation Session 4 1.30-1.55 1.55-2.20 Afternoon tea 2.30-3.00pm Paper presentation Session 5A and 5B 3.00-3.25 3.25-3.50 3.50-4.15 Conference close 4.15-4.30 Airport shuttle 4.30-6.30pm

SESSIONS

Session 1A - Medical Applications

• Alina Van, Valerie Gay, Paul Kennedy, Edward Barin and Peter Leijdekkers: Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees
• Mai Shouman, Tim Turner and Rob Stocker: Using Decision Tree for Diagnosing Heart Disease Patients
• Guohua Liang and Chengqi Zhang: Empirical Study of Bagging Predictors on Medical Data

Session 1B - Algorithms 1

• Md Geaur Rahman and Md Zahidul Islam: A Decision Tree-based Missing Value Imputation for Data Preprocessing
• Adil Bagirov, Andrew Yatsko, Andrew Stranieri and Herbert Jelinek: Feature Selection using Misclassification Counts
• Sona Taheri, Musa Mammadov and Adil M.Bagirov: Improving Naive Bayes Classifier Using Conditional Probabilities

Session 2A - Working with Text

• Poonam Goyal and Mehala N.: Concept Based Query Recommendation
• Henry Petersen and Josiah Poon: Enhancing Short Text Clustering with Small External Repositories
• Calum Robertson: Reassembling Multilingual Temporal News Datasets with Incomplete Information

Session 2B - Applications 1

• Mingjian Tang, Sumudu Mendis, Wayne Murray, Yingsong Hu and Alison Sutinen: Unsupervised Fraud Detection in Medicare Australia
• Yingsong Hu, D. Wayne Murray, Alison Sutinen, B. Sumudu U. Mendis and Mingjian Tang: Prescriber-Consumer Social Network Analysis for Risk Level Re-estimation based on an Asymmetrical Rating Exchange Model
• Yanbo Wang: Model Selection Strategy for Customer Attrition Risk Prediction in Retail Banking

Session 3A - Record Linkage and Privacy

• Dinusha Vatsalan and Peter Christen: An Efficient Two-Party Protocol for Approximate Matching in Private Record Linkage
• Rui Li, John Roddick and Denise De Vries: Bands of Privacy Preserving Objectives: Classification of PPDM Strategies
• Zhichun Fu, Peter Christen and Mac Boot: A Supervised Learning and Group Linking Method for Historical Census Household Linkage

Session 3B Applications 2

• Steven Burrows, Benno Stein, Jorg Frocte, David Wiesner and Katja Muller: Simulation Data Mining for Supporting Bridge Design
• Mamoun Alazab, Sitalakshmi Venkatraman, Paul Watters and Moutaz Alazab: Zero-day Malware Detection based on Supervised Learning Algorithms of API Call Signatures
• Mahmood Khan, Md Zahidul Islam and Mohsin Hafeez: Irrigation Water Demand Forecasting - A Data Pre-processing and Data Mining Approach Based on Spatio-Temporal Data

Session 4 - Algorithms 2

• Zahidul Islam: Knowledge Discovery through SysFor - a Systematically Developed Forest of Multiple Decision Trees
• Sattar Seifollahi, Adil Bagirov and John Yearwood: A novel hybrid neural learning algorithm using simulated annealing and quasisecant method

Session 5 - Algorithms 3

• Md Anisur Rahman and Md Zahidul Islam: Seed-Detective - A Novel Clustering Technique Using High Quality Seed for K-Means on Categorical and Numerical Attributes
• Bismita Srichandan and Rajshekhar Sunderraman: OO-FSG - An Object-Oriented Approach to Mine Frequent Subgraphs