SIT717 Enterprise Business Intelligence

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SIT717 Enterprise Business Intelligence

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SIT717 Enterprise Business Intelligence

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Course Code: SIT717
University: Deakin University

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Country: Australia


Make use of the knowledge and the skills you studied in this unit to prepare an oral presentation. You are required to choose one of the following three topics.
Your view on Data mining Tools or Systems and how it can be applied in business intelligence. Since 1990’s, lots of data mining tools and systems have been developed. Many tools could be used to accomplish the essential data mining tasks such as discovering knowledge for classification or association. Compare at least three data mining tools or systems from the aspect of at least one data mining functionality.
There are many data mining tools available on the Internet. Even some commercial products have free demo versions. You can get useful information from KDnuggets , Google, etc., for more resources. Your survey should address at least the function, the techniques, and the interface of the tool. You should also analyze the strength and weakness of the tool comparing against other tools.
Can we always trust what discovered by a data mining system? In this topic, you can express your view on reliability of discovered knowledge or of the discovery process. Data mining and its outcomes can be affected by a number of factors. It is interesting to find out when the discovered knowledge is reliable and what are the factors that could affect the reliability. Evaluation methods should be discussed in this topic.
Interesting/potential new applications of data mining to Business intelligence. In this topic, you can list at least two potential new applications of data mining to Business intelligence. You can explain what are the potential new applications. Why it is interesting. How it is used, and describe the way to solve the new application problem and what people could get out of the process and the potential benefits. You also identify the future trends and opportunities and perhaps also impact to our daily life and to our future society.
You must submit your completed presentation in the Dropbox in CloudDeakin and submission must follow the following requirements:

Your submission should be a power point presentation (.ppt) file with voice embedded in each slide.
Maximum number of total slides is 8 (including title slide, conclusion and reference slides)
Maximum total duration of the presentation (voice) is 8 Minutes.
All submission must be via assignment dropbox in CoudDeakin. Email submission are not allowed and removed without marking.
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.


Data Mining: The procedure of evaluating bulk amount of data present in the databases for generating new information (Braha 2013).
Data Mining Tools: The techniques that help a data miner to perform data mining.
Data Mining Tools
The best tools for data mining are as follows:
Konstanz Information Miner
Open source data analytics
Integrates components
Allows to create data flows
Transforms data
Rapid Miner
Data science software platform
Used for business applications
Supports machine learning process
Rapid prototyping
Results visualization
R Programming
Open source programming language
Software environment
Develops statistical software
Develops data analysis
Statistical computing and graphics
Reflection About Data Mining Tools
According to me, data mining tools help a data miner to perform data mining. There are several tools available in the market. I feel that KNIME is the best tool for data mining.
Therefore, it can be concluded that
 Data mining helps to analyze data for generating new information.
 Data mining tools help in data mining
 Several data mining tools are available like KNIME, WEKA, R Programming, Orange and RapidMiner.
 After comparing, it is seen that KNIME is the best tool.
Braha, D. ed., 2013. Data mining for design and manufacturing: methods and applications (Vol. 3). Springer Science & Business Media.
Hofmann, M. and Klinkenberg, R. eds., 2013. RapidMiner: Data mining use cases and business analytics applications. CRC Press.
Warr, W.A., 2012. Scientific workflow systems: Pipeline Pilot and KNIME. Journal of computer-aided molecular design, 26(7), pp.801-804.
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

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