You will use Weka ([login to view URL]) to mine actual data for a problem of interest. These could be data from
something of interest to the school, data acquired from the web, etc. You will design the data mining task, mine the data, and describe
your results. You also will research existing solutions to the problem, if any have been proposed or documented. Your own data and
results need not be on par with actual industry results; the goal is for you to get as realistic a hands-on experience as possible, given
the constraints of what you have learned.
In writing up/presenting your research, think of yourselves as analysts employed by or retained by a company (large or small) or by a
funding source (e.g., a venture capital firm or incubator), who wants to understand the state of the art for using data mining for the task
in question. Review what has been done to date on your problem. Consider as an example predictive analytics for on-line advertising
(which we will discuss in class): A venture capital firm considering funding on-line ad networks or ad-tech startups would need to
understand the state of the art in using data mining for targeting on-line advertising, when considering an idea for applying data
mining. Don’t worry too much about coming up with a novel idea. It is more important to develop the idea well (within the scope of
what we’ve discussed in class).
You should use the CRISP-DM “data mining process” to structure your research and write-up. Keep in mind that it may be ineffective
simply to proceed linearly through the steps, and this may need to be reflected in your analysis. You should interact with me from the
preparation of your initial ideas through your write-up, as a consulting group would interact with a firm or funding source in preparing
a research report. Use your imagination, prior experience, or ask me to help to fill in any gaps between the material available and what
you would be able to find out if you actually could interact with the client firm.
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