A dataset of @RDataMining Tweets extracted on 3 February 2016 is now available at Datasets. The dataset can be used for text mining purpose.
Seminar on Cyberbullying Detection by Applying AI Approaches, University of Canberra, 4:30pm Tuesday 2 Feb 2016
Topic: Cyberbullying Detection by Applying AI Approaches
Speakers: Prof. Chengqi Zhang and Dr. Guodong Long, University of Technology, Sydney
Organisers: The Canberra Data Scientists group and the Information Technology & Engineering Program, University of Canberra
Date and time: 4:30-6:00pm, Tuesday, 2 February 2016
Location: Teal Room, Inspire Centre (Building 25 on the map at http://www.canberra.edu.au/maps/campus-map)
RSVP URL: http://www.meetup.com/CanberraDataScientists/events/228252655/
Cyberbullying is the use of technology to bully a person or group with the intent to hurt them socially, psychologically or even physically. Currently there are many young people being cyberbullied or involving in cyberbullying activities, and the cyberbullying offence has been defined as criminal activity by law. To avoid the severe results (e.g. spirit trauma, or be charged as criminal), cyberbullying detection emerged to real-time proactively prevent cyberbullying by generating early warning. Most studies on Cyberbullying detection focus on key-words search and sentiment filtering on textual contents. All of them neglects the online conversation's rich context information including texts, networks, time and demographics. In this talk, we will introduce a novel solution for applying AI approaches to detect cyberbullying by exploiting rich heterogeneous context information.
Prof. Chengqi Zhang is a Research Professor of Information Technology at The University of Technology Sydney (UTS), an Honorary Professor of the University of Queensland (UQ), Director of the UTS Priority Research Centre for Quantum Computation & Intelligent Systems (QCIS). He is Alternative Dean of UTS Graduate Research School, Chairman of the Australian Computer Society National Committee for Artificial Intelligence and Chairman of IEEE Computer Society Technical Committee of Intelligent Informatics (TCII). Chengqi Zhang obtained his PhD degree from the University of Queensland in 1991, followed by a Doctor of Science (DSc – Higher Doctorate) from Deakin University in 2002. His key areas of research are Distributed Artificial Intelligence, Data Mining and its applications. He has published more than 200 refereed research papers and six monographs and edited 16 books. He has attracted 12 ARC grants of $4.7M. He is a Fellow of the Australian Computer Society (ACS) and a Senior Member of the IEEE Computer Society (IEEE).
Dr. Guodong Long has over 10 years experience on leading, developing and coordinating industry and research projects. Since joined UTS in 2010, he has practically led total five industry projects including three ARC Linkage projects. Before join in UTS in 2010, Dr Long has over 6 years industry work experience in IT company. He has strong system-wide knowledge of all computer-related, especially for architecture and design for artificial intelligent based systems; and have strong creativity on research methodology and real application systems. He currently leads a research team to conduct application-driven research by collaborating with industry partners. He obtained his BSc and MSc degree from National University of Defence Technology (NUDT) in 2002, 2008, and PhD degree from University of Technology Sydney (UTS) in 2014, all from computer science.
See slides of 10+ excellent tutorials at KDD 2015 at http://www.kdd.org/kdd2015/tutorial.html, incl.
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 let me know if you have access to neither of two sites below. Thanks.
CIKM Machine Learning Competition 2015 is centered around the AFL.
Participants are required to predict the outcomes of every match in the
2015 AFL season in two phases:
The 2015 Big Data Summit
9-10 August 2015
collocated with ACM KDD 2015, Sydney
We take this privilege opportunity to invite you to
participate in the 2015 Big Data Summit:
• Co-located with ACM KDD2015
• Plenary sessions and keynote speeches by world
industrial and academic leaders
• Big data best practices and highlights in Australia
and New Zealand
• “Big Data in China” Forum
• “Data Science in India” Forum
• “Big Data in Asia” Panel
The theme of this year’s Big Data Summit is “Data to
Since the Summit’s inception in 2012, we have seen
increasing interest and investment within both
industry and government in data-led innovation and
industralisation to deeply explore big data universe,
invent data science, train data engineers and scientists,
and develop the data economy.
This year’s event aims to provide analytics professionals
and academia with a global and regional perspective to
outline the big data research, education and development
in the Asia Pacific region, showcase best practices,
explore thought-provoking insights, and demonstrate
solutions and lessons learned across industry,
government and academia.
Who Should Attend?
• Data modellers and business analysts
• Analytics professionals
• Business decision makers
• Policy executives
• Senior Government Representatives
• Academics (including research students)
What Are the Trends and Topics?
• The Data2Economy Agenda: Challenges, Trends and
• Latest Scientific Development in Data and Analytics
• The progress and future of big data in Australia and
• The progress and future of big data in China
• The progress and future of data science in India
• Future of Data Science and Analytics Science
• Data Economy and Industrial Transformation
• Competency, Policies and Processes
• Data Analytics Case Studies and Showcases
Why You Should Attend?
Started in 2012, the 2013 and 2014 Big Data Summit
(Sydney and Canberra) attracted over 250-300
participants from industry, government and academia.
This annual Australian Summit provides a premier and
unique forum for bridging the gaps between academia,
industry and government, and independent insights on the
advancement, best practices, trends and controversies
about data science, big data and data economy.
With very prestigious speakers across academic, industry
and government from China, India, Australia, and USA
and Europe, the 2015 Summit will cover a broad spectrum
of big data and analytics aspects and domains. The three
regional Forums organized by India, China and ANZ will
present first-hand view about progress and opportunities
in the Asia Pacific region. The “Big Data in Asia” Panel
will feature world leaders from both the Asia Pacific and
global communities, to draw a big picture of big data
innovation, services, education and economy.
Co-located with ACM KDD2015 in Hilton Sydney, BDS2015
attracts global interest, will mark a unique and high
quality opportunity for you and your organization to grasp
the cutting-edge and thought-leading progress, network
with peers and thought leaders, and most importantly,
dig out more insights and value from your big data and
lift your competency in the increasingly competitive and
challenging market and environment.
For More Information
For more details about the Summit, please visit the Website
Registration to BDS2015 will be free of charge, please Check
and Register via www.bigdatasummit.co.
For any other inquiries about the Summit, please feel free to
I will run a workshop on R and Data Mining for students in the Master of Business Analytics course at Deakin University in Melbourne on Thursday 28 May. See workshop slides at Training.
Submission deadline of the 13th Australasian Data Mining Conference (AusDM 2015) has been extended to Thursday 30 April. See details at http://ausdm15.ausdm.org/.
Speaker: A/Prof. Robert Ackland, ANU
Date: Wednesday 29 April
Time: 5.30pm for a 6pm start
Where: SAS Offices, 12 Moore Street, Canberra, ACT 2600
RDataMining Group on LinkedIn has 10,000 members today! Join us for knowledge & experience sharing on R & Data Mining http://group.rdatamining.com