Database Reference
In-Depth Information
Table 2.14
Rule of an association detection model.
Rule ID
Consequent
Antecedents
Support %
Confidence %
Rule 1
Product 4
Product 1 then
57.1
75.0
Product 3
The support value represents the percentage of units of the analysis, here
unique customers, that had a sequence of the antecedents. In the above example
the support rises to 57.1%, since four out of seven customers purchased product 3
after buying product 1. Three of these customers purchased product 4 afterward.
Thus, the respective rule confidence figure is 75%. The rule simply states that after
acquiring product 1 and then product 3, customers have an increased likelihood
(75%) of purchasing product 4 next.
DETECTING UNUSUAL RECORDS WITH RECORD SCREENING MODELING
TECHNIQUES
Record screening modeling techniques are applied to detect anomalies or outliers.
The techniques try to identify records with odd data patterns that do not ''conform''
to the typical patterns of ''normal'' cases.
Unsupervised record screening modeling techniques can be used for:
• Data auditing, as a preparatory step before applying subsequent data mining
models.
• Discovering fraud.
Valuable information is not just hidden in general data patterns. Some-
times rare or unexpected data patterns can reveal situations that merit special
attention or require immediate action. For instance, in the insurance industry,
unusual claim profiles may indicate fraudulent cases. Similarly, odd money transfer
transactions may suggest money laundering. Credit card transactions that do no
fit the general usage profile of the owner may also indicate signs of suspicious
activity.
Record screening modeling techniques can provide valuable help in revealing
fraud by identifying ''unexpected'' data patterns and ''odd'' cases. The unexpected
cases are not always suspicious. They may just indicate an unusual, yet acceptable,
behavior. For sure, though, they require further investigation before being classified
as suspicious or not.
Search WWH ::
Custom Search