One-attribute-rule

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One-attribute-rule

The one-attribute-rule, or OneR, is an algorithm for finding association rules. According to Ross, very simple association rules, involving just one attribute in the condition part, often work well in practice with real-world data. The idea of the OneR (one-attribute-rule) algorithm is to find the one attribute to use to classify a novel datapoint that makes fewest prediction errors.

For example, to classify a car you haven't seen before, you might apply the following rule: If Fast Then Sportscar

As opposed to a rule with multiple attributes in the condition: If Fast And Softtop And Red Then Sportscar.

The algorithm is as follows:

For each attribute A:

    For each value V of that attribute, create a rule:
      1. count how often each class appears
      2. find the most frequent class, c
      3. make a rule "if A=V then C=c"
    Calculate the error rate of this rule

  Pick the attribute whose rules produce the lowest error rate

References http://www.dcs.napier.ac.uk/~peter/vldb/dm/node8.html

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