Call for participation: AusDM 2014, Brisbane, 27-28 November
********************************************************* 12th Australasian Data Mining Conference (AusDM 2014) Brisbane, Australia 27-28 November 2014 http://ausdm14.ausdm.org/ ********************************************************* 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 ============ Registration site: http://wired.ivvy.com/event/FM12AD/ Registration fees: Standard Registration: $495 Student Standard Registration: $320 If you are registering as a student, contact us via the email [email protected]<mailto:[email protected]> with an evidence of you being an active student. We will issue you a discount code for you to use the website. Keynotes ======== 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. Workshop ======== Half-day workshop on R and Data Mining, Thursday afternoon, 27 November Dr. Yanchang Zhao, RDataMining.com 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 http://www.rdatamining.com/docs. 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 RDataMining.com 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 =================== http://www.linkedin.com/groups/AusDM-4907891
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