Short Course on R and Data Mining, University of Canberra, Fri 7 Oct 2016

Short Course on R and Data Mining

Information Technology and Engineering, University of Canberra

Fees: There is no fees for the short course but seats are limited to 60 – so register early through http://www.meetup.com/CanberraDataSci/events/234168862/

Presenters: Dr Yanchang Zhao (Adjunct Professor, UC), Professor Dharmendra Sharma

Time: 9:30am – 12:30pm, Fri 7 Oct 2016

Room: 2B02 (Building 2, room B02, University of Canberra)

Map and Parking:

http://www.canberra.edu.au/maps/pdf-maps/PARKING-Casual.pdf

Course Outline:

The course will cover R programming, data exploration and visualisation, and data mining with R. It will cover four topics below in two sessions. Each 1.5-hour session will consist of presentations on two topics, followed by lab for students to do exercises.

– R Programming and Data Exploration and Visualisation with R

– Regression and Classification with R

– Association Rule Mining with R

– Text Mining with R — an Analysis of Twitter Data

Instructions, prerequisites and slides for the course are or will be available at http://www.rdatamining.com/training/uc.

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CFP: AusDM 2016 paper submission extended to 2 Sept

14th Australasian Data Mining Conference (AusDM 2016)
Canberra, Australia,
6-8 December 2016
URL: http://ausdm16.ausdm.org/
Join us on LinkedIn: http://www.linkedin.com/groups/AusDM-4907891

The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. AusDM’16 seeks to showcase: Research Prototypes; Industry Case Studies; Practical Analytics Technology; and Research Student Projects.

Publication and topics
We are calling for papers, both research and applications, and from both academia and industry, for presentation at the conference. Accepted papers will be published in an up-coming volume (Data Mining and Analytics 2016) of the Conferences in Research and Practice in Information Technology (CRPIT) series by the Australian Computer Society which is also held in full-text on the ACM Digital Library. AusDM invites contributions addressing current research in data mining and knowledge discovery as well as experiences, novel applications and future challenges.

Submission of papers
– Academic submissions: Regular academic submissions can be made in Research Track reporting on research progress, with a paper length of between 8 and 12 pages in CRPIT style.
– Industry submissions: Submissions can be made in the Application Track to report on specific data mining implementations and experiences in governments and industry projects. Submissions in this category can be between 4 and 8 pages in CRPIT style.
– Industry Showcase submissions: Submission from industry and government on an analytics solution that has raised profits, reduced costs and/or achieved other important policy and/or business outcomes can be made in this track with a one page Abstract only.

Online submission system
https://easychair.org/conferences/?conf=ausdm2016

Important Dates
Paper Submission: extended to 6pm, Friday 2 Sept 2016, Australian Eastern Standard Time (AEST)
Authors Notified: Monday 24 October 2016
Camera Ready Submission: Monday 7 November 2016
Conference Dates: 6-8 December 2016

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Seminar: Data Mining for Biosecurity Regulation, University of Canberra, Wednesday 10 Aug 2016

Topic: Data Mining for Biosecurity Regulation
Speaker: A/Prof. Andrew Robinson, Melbourne University
When: 4:30pm-5:30pm, Wednesday 10 Aug 2016
Where: 9A1 (Building 9, Room A1), University of Canberra. See UC map at http://www.canberra.edu.au/maps/pdf-maps/UC-Bruce-Campus-Map.pdf
RSVP: http://www.meetup.com/CanberraDataSci/events/233033578/

Abstract:
The Department of Agriculture and Water Resources (the department) seeks to mitigate the inherent biosecurity risk of various pathways by various control measures. This presentation focuses on the deployment of data-mining tools on a collection of data resources held by the department. The overall results of the data mining exercises were very encouraging; we developed statistically reliable models that produced operationally realistic predictions. We discuss the benefits and challenges of statistical analysis of operational data resources.

About the speaker:
Andrew Robinson is Reader and Associate Professor in applied statistics, and deputy director of the Centre of Excellence for Biosecurity Risk Analysis (CEBRA), at the University of Melbourne. Professor Robinson spends much of his time thinking about biosecurity at national borders, including analyzing inspection and interception data using statistical tools, designing and trialing inspection surveillance systems, developing metrics by which regulatory inspectorates can assess their performance, and discussing all of the above with interested parties. He is a co-author of three books: Introduction to Scientific Programming and Simulation Using R, Forest Analytics with R, and Methods of Statistical Model Estimation.

Homepage: http://www.ms.unimelb.edu.au/~apro@unimelb/

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Canberra Data Miners: Seminar on Text, Knowledge and Information Extraction, by Dr Lizhen Qu (NICTA), Canberra, 4:30-5:30pm, Tuesday 1 Sept

Topic: Text, Knowledge, and Information Extraction
Speaker: Dr. Lizhen Qu, Researcher at NICTA
Organizer: Canberra Data miners Meetup Group
Date and time: 4:30-5:30pm, Tuesday 1 Sept
Location: Teal Room of Inspire Centre, University of Canberra, Building 25, University of Canberra, Pantowora St, Bruce
RSVP URL:  http://www.meetup.com/CanberraDataMiners/events/224420305/
Slides: http://www.meetup.com/CanberraDataMiners/files/

Abstract:

Unstructured text is exploding at an astounding rate. Managing documents, mining interesting information from text, making decisions based on large volume of text impose a big challenge in this era. One solution is to apply information extraction (IE) techniques, which map unstructured text into structured knowledge representation, and store them into existing databases or knowledge bases. Then we can apply existing data analytics tools based on structured data for diverse purposes. In this talk, I will walk you through the core IE techniques such as named entity recognition, named entity disambiguation, and relation extraction, as well as their real-world applications. I will also cover our ongoing work regarding harvesting domain specific knowledge by using deep learning techniques.

Bio:

Dr. Lizhen Qu is currently a researcher at the Machine Learning Research Group of National ICT Australia (NICTA), a research fellow at Australian National University. He was an invited speaker at Machine Learning Summer School in Sydney in 2015. Prior to being employed at NICTA, Lizhen Qu was a post-doc at Max Planck Institute for Informatics. Dr. Qu completed his PhD doctorate qualification in Sentiment Analysis from Max Planck Institute for Informatics and University of Saarland.  In 2008, he received the Diploma degree from the Computer Science Department at Technical University of Kaiserslautern. His main research focus is in natural language processing (NLP), with particular emphasis on machine learning approaches. He is especially interested in devising deep learning models to extract structured representations of knowledge from unstructured text. More details about Dr. Qu can be found at https://www.nicta.com.au/category/research/machine-learning/people/lqu/.

Contact:
Yanchang Zhao
Organizer of Canberra Data Miners Group
Email: yanchang@rdatamining.com

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Slides of 10+ excellent tutorials at KDD 2015: Spark, graph mining and many more

by Yanchang Zhao
RDataMining.com

I attended the KDD 2015 conference in Sydney last week. At the conference, there were more than 10 tutorials and I went to two of them, which are 1) Graph-Based User Behavior Modeling: From Prediction to Fraud Detection, and 2) Large Scale Distributed Data Science using Apache Spark. Both tutorials were very popular and the rooms were full, with some audience standing and some sitting on the floor.

The speakers and the conference organizers kindly provided the tutorial slides online at http://www.kdd.org/kdd2015/tutorial.html. I strongly suggest you to have a look at the slides, if you haven’t attended the conference. Below are a list of tutorials at the conference.

– VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms
– Graph-Based User Behavior Modeling: From Prediction to Fraud Detection
– A New Look at the System, Algorithm and Theory Foundations of Large-Scale Distributed Machine Learning
– Dense subgraph discovery (DSD)
– Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach
– Big Data Analytics: Optimization and Randomization
– Big Data Analytics: Social Media Anomaly Detection: Challenges and Solutions
– Diffusion in Social and Information Networks: Problems, Models and Machine Learning Methods
– Medical Mining
– Large Scale Distributed Data Science using Apache Spark
– Data-Driven Product Innovation
– Web Personalization and Recommender Systems

Another good news is, most (if not all) presentations at KDD 2015 have been video recorded, so hopefully the videos will be available at its website soon.

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www2.rdatamining.com: a mirror site of RDataMining.com for Chinese users

RDataMining.com now has a mirror website at http://www2.rdatamining.com. Users in China can download RDataMining documents, code and data at above mirror site, if no access to http://www.rdatamining.com.

Note that RDataMining.com will still be the primary site and please visit www2.RDataMining.com only when you have no access to the primary site.

Please feel free to let me know if you have access to neither of two sites below. Thanks.
www .RDataMining.com
www2 .RDataMining.com

Contact: Yanchang Zhao <yanchang(at)rdatamining.com>

Posted in Data Mining, R | Tagged , | 1 Comment

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

*************************************************************
The 13th Australasian Data Mining Conference (AusDM 2015)
Sydney, Australia, 8–9 August 2015
URL: http://ausdm15.ausdm.org/
*************************************************************

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.

Keynotes
========

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:
http://www.linkedin.com/groups/AusDM-4907891

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