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<HTML> <HEAD> <!--SCRIPT LANGUAGE="JavaScript" SRC="http://a1835.g.akamai.net/f/1835/276/3h/www.netlibrary.com/include/js/dictionary_library.js"></SCRIPT> <SCRIPT LANGUAGE="JavaScript"> if (!opener){document.onkeyup=parent.turnBookPage;} </SCRIPT!--> <META HTTP-EQUIV="Cache-Control" CONTENT="no-cache"> <META HTTP-EQUIV="Pragma" CONTENT="no-cache"> <META HTTP-EQUIV="Expires" CONTENT="-1"><META http-equiv="Content-Type" content="text/html; charset=windows-1252"><SCRIPT>var PrevPage="Page_134";var NextPage="Page_136";var CurPage="Page_135";var PageOrder="145";</SCRIPT> <TITLE>Document</TITLE> </HEAD> <BODY BGCOLOR="#FFFFFF"><CENTER><TABLE BORDER=0 WIDTH=100% CELLPADDING=0><TR><TD ALIGN=CENTER> <TABLE BORDER=0 CELLPADDING=2 CELLSPACING=0 WIDTH=100%> <TR> <TD ALIGN=LEFT><A HREF='Page_134.html'>Previous</A></TD> <TD ALIGN=RIGHT><A HREF='Page_136.html'>Next</A></TD> </TR> </TABLE></TD></TR><TR><TD ALIGN=LEFT><P><A NAME='JUMPDEST_Page_135'/><A NAME='{488}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0 WIDTH='100%'><TR><TD ALIGN=RIGHT><FONT FACE='Times New Roman, Times, Serif' SIZE=2 COLOR=#FF0000>Page 135</FONT></TD></TR></TABLE><A NAME='{489}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5></TD> <TD COLSPAN=3 HEIGHT=12></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>As a general rule, neural networks work best on data sets with a large number of numeric attributes. Machine-learning algorithms incorporated in most decision tree and rule-generating data mining tools work best with data sets with a large number of records and a large number of attributes. Empirical studies have shown that the structure of the data critically impacts the accuracy of a data mining tool. For example, data sets with extreme distributions (skew > 1 and kurtosis > 7) and with many binary/categorical attributes (> 38%) tend to favor machine-learning-based data mining tools.</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{48A}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5></TD> <TD COLSPAN=3 HEIGHT=12></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>Often, derived ratios of input fields may be required in order to capture the impact or the true value of the inputs—to capture the velocity of a client value, such as profit or propensity to buy. For example, a commonly derived ratio is one of debt-to-income: rather than using simply the debt and income attributes as inputs, more can be gained by the ratio rather than the individual values. In your web analysis, the number of site visits or the number of purchases made over time may provide a better insight into the true value of website customers:</FONT></TD><TD></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR> <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{48B}'/><TABLE CELLPADDING=0 CELLSPACING=0 BORDER=0 WIDTH='100%'><TR><TD HEIGHT=12></TD></TR><TR><TD><TABLE CELLSPACING=0 WIDTH=576 CELLPADDING=5><TR><TD WIDTH=269 VALIGN=TOP><FONT FACE='Times New Roman, Times, Serif' SIZE=2>Number of purchases/Number of visits:</FONT></TD><TD WIDTH=307 VALIGN=TOP><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR> <TD ROWSPAN=5 WIDTH=0><IMG SRC='tab.gif' BORDER=0 WIDTH=0 HEIGHT=1></TD> <TD COLSPAN=3 HEIGHT=1></TD> <TD ROWSPAN=5></TD></TR><TR> <TD COLSPAN=3></TD></TR><TR><TD></TD> <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2>7/9 = .77
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