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Data Mining in Contracook
Purpose: To outline the data mining strategy that will be used in Contracook’s
recommendation functionality.
Introduction: Contracook is a website built to give the user a simple interface with
functional recommendations and searches for restaurants. The data for our ‘data mining’
is actually designed into the database, making it a more simple form of data analysis, and
more closely resembles ‘data dredging.’ An algorithm will be in place to logically make
use of the designed data fields, making a restaurant recommendation.
Strategy – Via use of bagging(averaging) I will be using predictive data mining in a
general sense, my algorithm will have the above logic built in, and will not be a learning
algorithm. Bagging will be useful in tags, and because I am dealing with a relatively
small set of data[1]. In addition, boosting will be used to properly weight all input
required to make the predictions necessary… on this note, the standard in boosting
procedure will avail, assigning greater weight to the rarer tags or other data[1].
Due to the lack of specific examples available, I am ordering Han, J., Kamber, M.
(2000). Data mining: Concepts and Techniques. New York: Morgan-Kaufman.
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