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In each of these cases, a random selection from the database would have provided more efficacious results than applying the association rule. With association rules, one needs to keep in mind the prior probabilities involved. To illustrate, we now ask Clementine to provide us with a priori association rules, but this time using the confidence [...]

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?Association rules need to be applied with care, since their results are sometimes deceptive. Lets look at an example. Turning back to the a priori algorithm, we asked Clementine to mine association rules from the adult database using 10% minimum support, 60% minimum confidence, and a maximum of two antecedents. The results are shown in [...]

?As mentioned above, GRI can handle numerical inputs as well as categori- cal inputs. We illustrate this using Clementine on the adult data set, instructing the GRI algorithm to accept both numerical variables and categorical variables as possi- ble antecedents (although, still, only categorical variables are possible consequents). The results, for minimum support and confidence [...]

For rules with more than one antecedent, p(x) is considered to be the probability of the conjunction of the variable values in the antecedent. As usual, the user specifies desired minimum support and confidence criteria.

The structure of association rules, where the antecedent and consequent are both Boolean statements, makes them particularly well suited for handling categorical data, as we have seen. However, what happens when we try to extend our association rule mining to a broader range of data, specifically, numerical attributes? Of course, it is always possible to [...]

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Thus far, we have examined association rules using flag data types only. That is, all of the vegetable stand attributes took the form of Boolean 0/1 flags, resulting in the tabular data format found in Table 10.3, reflecting a straightforward market basket analysis problem. However, association rules are not restricted to flag data types. In [...]

Compare Table 10.7 with Figure 10.1, the association rules reported by Clementines version of the a priori algorithm, with minimum 80% confidence, and sorted by support confidence. The first column indicates the number of instances the antecedent occurs in the transactions. The second column, which Clementine calls support, is actually not what we defined [...]

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