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- ?WHEN NOT TO USE ASSOCIATION RULES (2)
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DO ASSOCIATION RULES REPRESENT SUPERVISED OR UNSUPERVISED LEARNING?
July 2nd, 2009Before we leave the subject of association rules, let us touch on a fewtopics of interest. First, we may ask whether association rules represent supervised or unsupervised learning. Recall that most data mining methods represent supervised learning, since (1) a target variable is prespecified, and (2) the algorithm is provided with a rich collection of [...]
?WHEN NOT TO USE ASSOCIATION RULES (2)
June 29th, 2009In 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 [...]
Modify Your Home Loan Now
June 27th, 2009Whenever you have problems to buy a house, a home loan service will definitely give you a lot of way outs in the end. But whether you recognize it or not, there will be a time when you fell like going down and become unable to finish the loan very well right away. If you [...]
?WHEN NOT TO USE ASSOCIATION RULES
June 26th, 2009?Association rules need to be applied with care, since their results are sometimes deceptive. Let’s 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 [...]
Application of Generalized Rule Induction (2)
June 23rd, 2009?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 [...]
J-Measure
June 20th, 2009For 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.
INFORMATION-THEORETIC APPROACH: GENERALIZED RULE INDUCTION METHOD
June 17th, 2009The 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 [...]
CRM Software for Efficient Sales Team
June 15th, 2009Sales are important part of a company. If you have best sales team and marketing you will have best result that will benefits the whole company or business. That’s why you need to do many things to improve the ability of the whole sales people in your company or business. If you need a help [...]
?EXTENSION FROM FLAG DATA TO GENERAL ?CATEGORICAL DATA
June 14th, 2009Thus 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 [...]
HOWDOES THE A PRIORI ALGORITHM WORK (PART 2)? GENERATING ASSOCIATION RULES (2)
June 11th, 2009Compare Table 10.7 with Figure 10.1, the association rules reported by Clementine’s 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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