Applied Mathematicsematics

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Utilized information Mining for enterprise and through Giudici, Paolo, Figini, Silvia [Wiley,2009] (Paperback) second variation [Paperback]

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Extra resources for Applied Data Mining for Business and Industry, 2nd edition

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Yk∗ x1∗ x2∗ .. xi∗ .. xh∗ nxy (x1∗ , y1∗ ) nxy (x2∗ , y1∗ ) .. nxy (xi∗ , y1∗ ) .. nxy (xh∗ , y1∗ ) ny (y1∗ ) nxy (x1∗ , y2∗ ) nxy (x2∗ , y2∗ ) .. nxy (xi∗ , y2∗ ) .. nxy (xh∗ , y2∗ ) ny (y2∗ ) ... .. ... . ... nxy (x1∗ , yj∗ ) nxy (x2∗ , yj∗ ) .. nxy (xi∗ , yj∗ ) .. nxy (xh∗ , yj∗ ) ny (yj∗ ) ... .. ... . ... nxy (x1∗ , yk∗ ) nxy (x2∗ , yk∗ ) .. nxy (xi∗ , yk∗ ) .. nxy (xh∗ , yk∗ ) ny (yk∗ ) nx (x1∗ ) nx (x2∗ ) .. nx (xi∗ ) .. nx (xh∗ ) N 28 APPLIED DATA MINING FOR BUSINESS AND INDUSTRY number of observations that assume the j th level of Y (j = 1, 2, .

Var(Xh ) ... order to use the covariance as an exploratory index it is necessary to normalise it, so that it becomes a relative index. It can be shown that the maximum value that Cov(X, Y ) can assume is σx σy , the product of the two standard deviations of the variables. On the other hand, the minimum value that Cov(X, Y ) can assume is −σx σy . Furthermore, Cov(X, Y ) takes its maximum value when the observed data lie on a line with positive slope and its minimum value when all the observed data lie on a line with negative slope.

This topic also remains an active research area, and the existence of a large number of indexes shows that the subject has yet to be consolidated. We put the available indexes into three principal classes: distance measures, dependence measures and model-based indexes. Distance measures are applicable to any contingency table. Dependence measures, in contrast, give precise information on the type of dependence among the variables under examination, but are hardly applicable to contingency tables of dimension greater than 2.

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