Call for participation: AusDM 2014, Brisbane, 27-28 November

12th Australasian Data Mining Conference (AusDM 2014)
Brisbane, Australia
27-28 November 2014

The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. Since AusDM’02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments.

This year’s conference, AusDM’14 builds on this tradition of facilitating the cross-disciplinary exchange of ideas, experience and potential research directions. Specifically, the conference seeks to showcase: Industry Case Studies; Research Prototypes; Practical Analytics Technology; and Research Student Projects. AusDM’14 will be a meeting place for pushing forward the frontiers of data mining in industry and academia. We have lined up an excellent Keynote Speaker program.


Registration site:
Registration fees:
Standard Registration: $495
Student Standard Registration: $320

If you are registering as a student, contact us via the email with an evidence of you being an active student. We will issue you a discount code for you to use the website.


Keynote I: Learning in sequential decision problems
Prof. Peter Bartlett, University of California, Berkeley, USA

Abstract: Many problems of decision making under uncertainty can be formulated as sequential decision problems in which a strategy’s current state and choice of action determine its loss and next state, and the aim is to choose actions so as to minimize the sum of losses incurred.  For instance, in internet news recommendation and in digital marketing, the optimization of interactions with users to maximize long-term utility needs to exploit the dynamics of users. We consider three problems of this kind: Markov decision processes with adversarially chosen transition and loss structures; policy optimization for large scale Markov decision processes; and linear tracking problems with adversarially chosen quadratic loss functions. We present algorithms and optimal excess loss bounds for these three problems. We show situations where these algorithms are computationally efficient, and others where hardness results suggest that no algorithm is computationally efficient.

Keynote II: Making Sense of a Random World through Statistics
Prof. Geoff McLachlan, University of Queensland, Brisbane, Australia

Abstract: With the growth in data in recent times, it is argued in this talk that there is a need for even more statistical methods in data mining. In so doing, we present some examples in which there is a need to adopt some fairly sophisticated statistical procedures (at least not off-the-shelf methods) to avoid misleading inferences being made about patterns in the data due to randomness. One example concerns the search for clusters in data. Having found an apparent clustering in a dataset, as evidenced in a visualisation of the dataset in some reduced form, the question arises of whether this clustering is representative of an underlying group structure or is merely due to random fluctuations. Another example concerns the supervised classification in the case of many variables measured on only a small number of objects. In this situation, it is possible to construct a classifier based on a relatively small subset of the variables that provides a perfect classification of the data (that is, its apparent error rate is zero). We discuss how statistics is needed to correct for the optimism in these results due to randomness and to provide a realistic interpretation.


Half-day workshop on R and Data Mining, Thursday afternoon, 27 November
Dr. Yanchang Zhao,

The workshop will present an introduction on data mining with R, providing R code examples for classification, clustering, association rules and text mining. See workshop slides at

Accepted Papers

Comparison of athletic performances across disciplines and disability classes
Chris Barnes

Factors Influencing Robustness and Effectiveness of Conditional Random Fields in Active Learning Frameworks
Mahnoosh Kholghi, Laurianne Sitbon, Guido Zuccon and Anthony Nguyen

Tree Based Scalable Indexing for Multi-Party Privacy Preserving Record Linkage
Thilina Ranbaduge, Peter Christen and Dinusha Vatsalan

Towards Social Media as a Data Source for Opportunistic Sensor Networking
James Meneghello, Kevin Lee and Nik Thompson

A Case Study of Utilising Concept Knowledge in a Topic Specific Document Collection
Gavin Shaw and Richi Nayak

An Efficient Tagging Data Interpretation and Representation Scheme for Item Recommendation
Noor Ifada and Richi Nayak

Evolving Wavelet Neural Networks for Breast Cancer Classification
Maryam Khan, Stephan Chalup and Alexandre Mendes

Dynamic Class Prediction with Classifier Based Distance Measure
Senay Yasar Saglam and Nick Street

Detecting Digital Newspaper Duplicates with Focus on eliminating OCR errors
Yeshey Peden and Richi Nayak

Improving Scalability and Performance of Random Forest Based Learning-to-Rank Algorithms by Aggressive Subsampling
Muhammad Ibrahim and Mark Carman

A Multidimensional Collaborative Filtering Fusion Approach with Dimensionality Reduction
Xiaoyu Tang, Yue Xu, Ahmad Abdel-Hafez and Shlomo Geva

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

Pruned Annular Extreme Learning Machine Optimization based on RANSAC Multi Model Response Regularization
Lavneet Singh and Girija Chetty

Automatic Detection of Cluster Structure Changes using Relative Density Self-Organizing Maps
Denny, Pandu Wicaksono and Ruli Manurung

Decreasing Uncertainty for Improvement of Relevancy Prediction
Libiao Zhang, Yuefeng Li and Moch Arif Bijaksana

Identifying Product Families Using Data Mining Techniques in Manufacturing Paradigm
Israt Jahan Chowdhury and Richi Nayak

Market Segmentation of EFTPOS Retailers
Ashishkumar Singh, Grace Rumantir and Annie South

Locality-Sensitive Hashing for Protein Classification
Lawrence Buckingham, James Hogan, Shlomo Geva and Wayne Kelly

Real-time Collaborative Filtering Recommender Systems
Huizhi Liang, Haoran Du and Qing Wang

Pattern-based Topic Modelling for Query Expansion
Yang Gao, Yue Xu and Yuefeng Li

Hartigan’s Method for K-modes Clustering and Its Advantages
Zheng Rong Xiang and Zahidul Islam

Data Cleansing during Data Collection from Wireless Sensor Networks
Md Zahidul Islam, Quazi Mamun and Md Geaur Rahman

Content Based Image Retrieval Using Signature Representation
Dinesha Chathurani Nanayakkara Wasam Uluwitige, Shlomo Geva, Vinod Chandran and Timothy Chappell

Organising Committee

Conference Chairs
Richi Nayak, Queensland University of Technology, Brisbane, Australia
Paul Kennedy, University of Technology, Sydney

Program Chairs (Research)
Lin Liu, University of South Australia, Adelaide
Xue Li, University of Queensland, Brisbane, Australia

Program Chairs (Application)
Kok-Leong Ong, Deakin University, Melbourne
Yanchang Zhao, Department of Immigration & Border Protection, Australia; and

Sponsorship Chair
Andrew Stranieri, University of Ballarat, Ballarat

Local Chair
Yue Xu, Brisbane, Australia

Steering Committee Chairs
Simeon Simoff, University of Western Sydney
Graham Williams, Australian Taxation Office

Other Steering Committee Members
Peter Christen, The Australian National University, Canberra
Paul Kennedy, University of Technology, Sydney
Jiuyong Li, University of South Australia, Adelaide
Kok-Leong Ong, Deakin University, Melbourne
John Roddick, Flinders University, Adelaide
Andrew Stranieri, University of Ballarat, Ballarat
Geoff Webb, Monash University, Melbourne

Join us on LinkedIn


About Yanchang Zhao

I am a data miner, using R for data mining applications. My work on R and data mining:; Twitter; Group on Linkedin; and Group on Google.
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