Demonstration – 4ft-Miner



In this part we will show an example of 4ftTask. We will use data of fictious bank Barbora where are 6181 objects. For our analysis we will use these attributes devided into partial cedents:

Client with two attributes:

Social Status with two attributes:

Loan with three attributes:

Loan Quality with one attribute:

Figure 1: Example

Figure 1: Input parameters

We solve the task What combinations of characteristics of a client (antecedent) lead to good loan. We can write this task as an association rule in the form:

Client Social Status Region Loan Quality(good) / Loan

Figure 1 shows our task in general. Here can be seen that as quantifier is used Double Founded Implication with p value 0.700. Parameter Base is set 20. Antecedent consists of two partial cedents (Client, Social Status). These partial cedents for antecedent can be seen in detail on next three figures. From each figure can be seen all necessary parameters as length of partial cedents and all parameters for each attribute.

Figure 2: Client

Figure 2: Partial antecedent – Client

Figure 3: Social Status

Figure 3: Partial antecedent – Social Status

Succedent consist of one partial cedent. This partial cedent is on the next figure. Here can be seen that we will examine only one category of the attribute Loan Qualitygood

Figure 4: Loan Quality

Figure 4: Partial succedent – Loan Quality

Condition consists again of one partial cedent (see next figure).

Figure 5: Loan

Figure 5: Partial condition – Loan

After generating this task we get 10 hypothesis and one of them is displayed on fig. 6. This hypothesis says that Men with low salary living in Havlickuv Brod with condition that they borrow 100–250 thousands crowns and they have to repay it in 1–2 years are bad borrowers.

Figure 6: Hypothesis

Figure 6: Hypothesis

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