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[Description]: This assignment is designed to provide students a good opportunity to read some materials to understand the theory, methods and recent development in the area of business intelligence, especially the modern technologies that are normally used in business intelligence. The typical technologies that are frequently applied in business intelligence come from the data mining and machine learning areas.
In this assignment, you are required to choose one of the following areas, and write a survey on a topic you have chosen in that area:
1. Association rule mining
2. Unsupervised learning
3. Supervised learning
4. Outlier/Anomaly detection
5. Ensemble learning
[Tasks and Requirements]:
Your survey should meet the following requirements:
(1) Should include the following components:
A. A meaningful title followed by your name and student ID
B. An abstract
C. An introduction
D. Body of the Survey
In this section, you need to cite publications/papers that you use, which can include conference proceedings, journal articles, books/book chapters, technical reports or any other publications. You need to clearly state what the paper is about (this could be a theory, an idea, a hypothesis, an assumption, a proposal; it could also be a method, an approach, a technique, an algorithm etc.). How it works (technical details). How it can be used in business intelligence. You can provide comparisons of different methods. Identify the advantages and disadvantages of each method. You could state your idea, your opinion, your view, and/or the other people’s view or opinion about the work/method in the paper.
E. Conclusion/Summary
F. List of Reference ( Please check Deakin Guide to Referencing)
(2) Should be no less than 3000 words and no more than 5000 words (including references)
(3) You are required to read at least 5 references including lecture notes of this unit.
(4) Only references that are used/discussed in your survey should be included in the list of Reference.
Page 1 of 3
[Submission]:
• You must submit your completed document (pdf or word doc) in the Dropbox in
CloudDeakin.
• Remember that late submissions will be penalised. Further, the CloudDeakin server is the
ultimate time keeper when it comes to determining whether your submission has been
received on time.
• You are also reminded to keep a backup copy for record.
[Readings and possible reference]:
All students are required to read at least three articles/papers in the area of the topic you have
chosen. In the following list, we have provided more articles, and the links lead to top international
Journals and conferences in the area. It provides you a good opportunity to know the recent
developments that are happening in those areas.
• Association Rule Mining
• Association Analysis: Basic Concepts and Algorithms
• Mining Association Rules
• Association Rules and AprioRi Algorithm
• Unsupervised Learning
• Shimon Ullman and Tomasa Poggio, Unsupervised Learning.
• Zoubin Ghahramani, Unsupervised Learning
• Wikipedia Definition, Unsupervised learning
• Supervised Learning
• Supervised learning
• What is supervised learning
• What is the difference between supervised learning and unsupervised learning?
Page 2 of 3
• Outlier/Anomaly detection
• Wikipedia Definition: Anomaly detection
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson
Higher Ed, 2013.
• Following are two survey papers in this area. In your work, you should also include
new articles that are not used in these survey papers.
o Hodge, V.J. & Austin, A Survey of Outlier Detection Methodologies, J. Artif
Intell Rev (2004) 22: 85. doi:10.1007/s10462-004-4304-y
o Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly
detection: A survey. ACM Comput. Surv. 41, 3, Article 15 (July 2009), 58
pages. DOI=http://dl.acm.org/citation.cfm?id=1541882
• Ensemble Learning
• Wikipedia Definition, Ensemble learning
• Thomas G Dietterich, Ensemble Methods in Machine Learning
• Bagging, boosting and stacking in machine learning.
• Ensemble Learning.
International Conferences
1. ICML – International Conference on Machine Learning, ICML2017, ICML 2016, ICML
2015
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