Transcription of Scoring the Data Using Association Rules
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Scoring the Data Using Association Rules
Scoring the data Using Association Rules Abstract In many data mining applications, the objective is to select data cases of a target class. For example, in direct marketing, marketers want to select likely buyers of a particular product for promotion. In such applications, it is often too difficult to predict who will definitely be in the target class ( , the buyer class) because the data used for modeling is often very noisy and has a highly imbalanced class distribution. Traditionally, classification systems are used to solve this problem. Instead of classifying each data case to a definite class ( , buyer or non-buyer), a classification system is modified to produce a class probability estimate (or a score ) for the data case to indicate the likelihood that the data case belongs to the target class ( , the buyer class). However, existing classification systems only aim to find a subset of the regularities or Rules that exist in data .
1. to score each case in the test (or future) data, e.g., to assign a probability estimate to indicate how likely the case belongs to the positive class, and 2. to rank the cases in the test set using their scores.
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