See my latest slides on Association Rule Mining with R at
It is one of my tutorials on Machine Learning with R for the Melbourne Data Science Week on 1 June 2017. If you are interested, details can be found at
AusDM 2017 will be a special event this year being held in conjunction with IJCAI in Melbourne. This is a tremendous opportunity to present data mining research from Australia to a wider audience, with collaborative arrangements with IJCAI to invite wider participation.
Submissions are required by 5pm Monday 22 May 2017. Visit http://ausdm17.ausdm.org for details.
14th Australasian Data Mining Conference (AusDM 2016)
6-8 December 2016
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
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
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/
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.
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/.
Organizer of Canberra Data Miners Group