аЯрЁБс>ўџ <>ўџџџ;џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџьЅС%` №ПКbjbj•й•й *їГїГК џџџџџџЄўўўўўўўк к к к ц 6 n :@ @ @ @ @ @ @ ЕЗЗЗЗЗЗ$Є!h $Tлў@ @ @ @ @ лўў@ @ №ddd@ Fў@ ў@ Еd@ ЕddЊщ|ўўa@ њ pVykі˜Шк † Re4 06 wъ`$и ‚`$$a`$ўa @ @ d@ @ @ @ @ ллZ @ @ @ 6 @ @ @ @ фі фі ўўўўўўџџџџ Spring 2008 Assignment# 4 Due on 04/18/2008 by 11.55 p.m. Part I Solve Problem 6, 7, 8 & 9 from Chapter 6 of Tan, Steinbach and Kumar’s book. Part II Please read the document HYPERLINK "http://www.utdallas.edu/~lkhan/Summer2004Mining/bank-data.txt"Data Preparation and Mining with WEKA. It provides a detailed example of the preprocessing steps and association rule mining with WEKA based on this problem. The marketing department of a financial firm keeps records on customers, including demographic information and, number of type of accounts. When launching a new product, such as a "Personal Equity Plan" (PEP), a direct mail piece, advertising the product, is sent to existing customers, and a record kept as to whether that customer responded and bought the product. Based on this store of prior experience, the managers decide to use data mining techniques to build customer profile models. In this particular problem we are interested only in deriving (quantitative) association rules from the data (in a future assignment we will consider the use of classification. The data contains the following fields ida unique identification numberageage of customer in yearssexMALE / FEMALEregionInner_city/rural/suburban/townincomeincome of customermarriedis the customer married (YES/NO)childrennumber of childrencardoes the customer own a car (YES/NO)save_acctdoes the customer have a saving account (YES/NO)current_acctdoes the customer have a current account (YES/NO)mortgagedoes the customer have a mortgage (YES/NO)pepdid the customer buy a PEP after the last mailing (YES/NO)The data is contained in the file  HYPERLINK "http://maya.cs.depaul.edu/~classes/ect584/hw/bank-data.txt" bank-data.txt. Each record is a customer description where the "pep" field indicates whether or not that customer bought a PEP after the last mailing. Your goal is to perform Association Rule discovery on the data set using the Weka package. Note: Association rule discovery requires discretization of continuous variables. This task can be performed in the data transformation step or (in some cases) by the mining program. WEKA is a full data mining suite which include various preprocessing modules. When using WEKA, you will first apply the relevant preprocessing filters to transform the data before you perform association rule discovery (see the document  HYPERLINK "http://maya.cs.depaul.edu/~classes/ect584/WEKA/index.html" Data Preparation and Mining with WEKA. First use Excel or other spreadsheet program to examine and transform the data. For example, the "id" field can be removed. Furthermore, you may divide the "age" attribute into 3 groups: less than 25, between 25 and 45, and greater than 45. Similarly, the "income" attribute can be divided into groups: less than 15K, between 15k and 30K, and greater than 30k), and the "children" attributes into groups 0, 1, and greater than 1. Please clearly specify any transformations you perform on the data. 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