Parameters | Pattern Discovery | Level of Measurement

Level of Measurement
Use the drop-down menu to select the assumed type of levels of the variables. The Nominal method is selected by default. Levels of Measurement
Measurement of some attribute of a set of objects is the process of assigning numbers or other symbols to the objects in such a way that properties of the numbers or symbols reflect properties of the attribute being measured. There are different levels of measurement that involve different properties (relations and operations) of the numbers or symbols. Associated with each level of measurement is a set of transformation of the measurements that preserve the relevant properties; these transformations are called permissible transformations. A particular way of assigning numbers or symbols to measure something is called a scale of measurement.
Available options are described in the table below:
 Level of Measurement Description Interval Objects are assigned numbers such that the order of the numbers reflects an order relation defined on the attribute. Values are assumed to be numeric and continuous. Ratio Objects are assigned numbers such that differences and ratios between the numbers reflect differences and ratios of the attribute. Values are assumed to be numeric and positive. Ordinal Objects are assigned numbers such that the order of the numbers reflects an order relation defined on the attribute. Values are assumed to form ordered categories. Nominal Two objects are assigned the same symbol if they have the same value of the attribute. Values are assumed to form non-ordered categories. Binary variables are considered to be symmetric when both outcomes are equally important. In this case, it does not matter whether a variable is assigned the symbol “1” or “0”. Asymmetric Nominal Two objects are assigned the same symbol if they have the same value of the attribute. Values are assumed to form non-ordered categories. Binary variables, in this case, are considered asymmetric when the two outcomes are not equally important. In this case, the most important outcome (usually defined as “present”) is assigned the value of “1” and the least important outcome (usually defined as “absent”) is assigned the value of “0”. If a variable is defined as an asymmetric nominal variable and two data units score the same but fall into the “absent” category, the absent-absent match is excluded from the computation of the distance metric.
To Specify This Option: Select the desired level of measurement using the drop-down menu.