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Data Mining

WizSoft has developed a proprietary algorithm for revealing if-then rules (association rules) in a given data. The algorithm quickly reveals all the if-then rules that meet user-defined thresholds. An example of an if-then rule:

If City is NYC

and Amount Purchased is 200 … 300 (average=250)

and salesperson is Dave

Then

Growth Since Last Year is less than 0

Rule’s Probability: 0.70

The rule exists in 3,700 records

Significance Level: Error probability, 0.001

The last three numbers denotes the rule parameters:

The Rule’s Probability (sometimes known as the confidence level) designates the ratio between the number of records in which both the condition(s) and the result hold, and the corresponding number of records in which the condition(s) hold with or without the result.

The second number (sometimes called the support level) is simply the number of records where both the condition(s) and result hold.

The third number Significance Level indicates the degree of the rule’s validity. It is equal to 1 minus the error probability which qualifies the probability that the rule exists accidentally in the data under analysis.