Call for participation: AusDM 2015, Sydney, 8-9 August

[This article was first published on, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The 13th Australasian Data Mining Conference (AusDM 2015)
Sydney, Australia, 8–9 August 2015

The Australasian Data Mining Conference is devoted to the art and science of intelligent data mining: the meaningful analysis of (usually large) data sets to discover relationships and present the data in novel ways that are compact, comprehensible and useful for researchers and practitioners.

This conference will bring together the Data Mining and Business Analytics community researchers and practitioners to share and learn of research and progress in the local context and new breakthroughs in data mining algorithms and their applications.


Discovering Negative Links on Social Networking Sites
Prof Huan Liu, Arizona State University

Large Scale Metric Learning using Locality Sensitive Hashing
Prof Ramamohanarao Kotagiri, University of Melbourne

Big Data for Everyone
Prof Jian Pei, Simon Fraser University

Big Data Mining and Data Science
Prof Yong Shi, Chinese Academy of Sciences

Deep Broad Learning – Big Models for Big Data
Prof Geoff Webb, Monash University

Algorithm acceleration for high throughout biology
Prof Wei Wang, University of California, Los Angeles

Big Data Analytics in Business Environments
Prof Hui Xiong, State University of New Jersey

On Mining Heterogeneous Information Networks
Prof Phillip Yu, University of Illinois at Chicago

Resource Management in Cloud Computing Systems
Prof Albert Zomaya, University of Sydney

Big Data Algorithms and Clinical Applications
A/Prof Yixin Chen, Washington University

Defining Data Science
Prof Yangyong Zhu, Fudan University

Learning with Big Data by Incremental Optimization of Performance Measures
Prof Zhi-Hua Zhou, Nanjinf University

Accepted Papers

Research Track:

FSMEC: A Feature Selection Method based on the Minimum Spanning Tree and Evolutionary Computation
Amer Abu Zaher, Regina Berretta, Ahmed Shamsul Arefin and Pablo Moscato

Mining Productive Emerging Patterns and Their Application in Trend Prediction
Vincent Mwintieru Nofong

Detection of Structural Changes in Data Streams
Ross Callister, Mihai Lazarescu and Duc-Son Pham

Multiple Imputation on Partitioned Datasets
Michael Furner and Md Zahidul Islam

Particle Swarm Optimisation for Feature Selection: A Size-Controlled Approach
Bing Xue and Mengjie Zhang

Complement Random Forest
Md Nasim Adnan and Zahid Islam

Aspect-Based Opinion Mining from Product Reviews Using Conditional Random Fields
Amani Samha, Yuefeng Li and Jinglan Zhang

On Ranking Nodes using kNN Graphs, Shortest-paths and GPUs
Ahmed Shamsul Arefin, Regina Berretta and Pablo Moscato

Link Prediction and Topological Feature Importance in Social Networks
Stephan Curiskis, Thomas Osborn and Paul Kennedy

AWST: A Novel Attribute Weight Selection Technique for Data Clustering
Md Anisur Rahman and Md Zahidul Islam

Genetic Programming Using Two Blocks To Extract Edge Features
Wenlong Fu, Mengjie Zhang and Mark Johnston

Designing a knowledge-based schema matching system for schema mapping
Sarawat Anam and Byeong Ho Kang

A Differentially Private Decision Forest
Sam Fletcher and Md Zahidul Islam

Industry Track:

Improving Bridge Deterioration Modelling Using Rainfall Data from the Bureau of Meteorology
Qing Huang, Kok-Leong Ong and Damminda Alahakoon

An Industrial Application of Rotation Forest: Transformer Health Diagnosis
Tamilalagan Natarajan, Duc-Son Pham and Mihai Lazarescu

Non-Invasive Attributes Significance in the Risk Evaluation of Heart Disease Using Decision Tree Analysis
Mai Shouman and Tim Turner

An Improved SMO Algorithm for Credit Risk Evaluation
Jue Wang, Aiguo Lu and Xuemei Jiang

Join us on LinkedIn:

To leave a comment for the author, please follow the link and comment on their blog: offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)