◤SD4ft-Miner Demonstration - Selection of the attributes◢
Main menu:
This section continues in explanation of the creating new task part described in previous section. Here will be shown how to:
By cedent are meant antecedent, succedent and condition. Special features of the SD4ft-Miner are First set and Second set (as can be seen from fig. 1) but they are entered the same way as all other cedents so they will be described together as cedents. Each cedent consists of one or more partial cedents. There is already created one partial cedent called antecedent, succedent, condition, first set or second set. It only depends on user if another partial cedent will be used. Sometimes it is useful to use more partial cedents especially if it is desirable to influence more the final hypothesis. Each partial cedent has as a parametr minimal and maximal length. This length determine the minimum and maximum number of attributes (for each partial cedent) that will be in final hypotheses.
For example: if we have these three attributes (age, height, weight) in one partial cedent and the minimum length is set 1 and maximum length is set 2 than there will be only these combinations of attributes in final hypotheses:
Figure 1: Task window
As was written above, SD4ft-Miner is similar to 4ft-Miner in the way that it seeks for association rules of the type:
Antecedent ≈ Succedent / Condition
But in the case of SD4ft-Miner is this rule applied on two different sets and so the previous formula is of the form:
Antecedent ≈ Succedent / Condition ∧ First set
Antecedent ≈ Succedent / Condition ∧ Second set
Association rules are computed for both sets and than are these computed values compared through SD4ft quantifiers such as difference of founded implication, difference of equivalence etc.
When partial cedents are defined it is possible to fill them up with attributes. For each attribute it is possible to set some parameters as can be seen from fig. 2.
Literal type is the first parameter and it has just two possibilities. Basic or Remaining. This parametr has reason when there are more then one attribut in partial cedent and can be combinated with length of the partial cedent for better results. Purpose of this parameter is, that at least one attribute set as basic will be in final hypothesis. Not every attribute set as basic but at least one.
Another parameter is Gace type. Gace type has three possibilities as can be seen from fig. 2 (positive, negative, both). Both means positive and negative. If positive gace type is selected then the attribute will be in final hypothesis in its positive form. If negative is selected then will be negation of this attribute in the final hypothesis.
The last parameter is Coefficient type. Coefficient types will be described as an example, so we suppose that these coefficients of attribute are given: 1, 2, 3 and the length of coefficient is set: minimum length = 1 and maximum length = 2:
Classes of equivalence is useful tool when it is not desirable to have some group of attributes together in one hypothesis. Classes of equivalence will ensure, that there will be displayed only hypotheses where there will be only one attribut from the class of equivalence in the cedent.
Figure 2: Attribute's parameters