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Why analytical characterization and attribute relevance analysis are needed and how these can be performed?
It is a statistical approach for preprocessing data to filter out irrelevant attributes or rank the relevant attribute. Measures of attribute relevance analysis can be used to recognize irrelevant attributes that can be unauthorized from the concept description process. The incorporation of this preprocessing step into class characterization or comparison is defined as an analytical characterization.
Data discrimination makes discrimination rules which are a comparison of the general features of objects between two classes defined as the target class and the contrasting class.
It is a comparison of the general characteristics of targeting class data objects with the general characteristics of objects from one or a set of contrasting classes. The user can define the target and contrasting classes. The methods used for data discrimination are very similar to the approaches used for data characterization with the exception that data discrimination results include comparative measures.
Reasons for attribute relevance analysis
There are several reasons for attribute relevance analysis are as follows −
It can decide which dimensions must be included.
It can produce a high level of generalization.
It can reduce the number of attributes that support us to read patterns easily.
The basic concept behind attribute relevance analysis is to evaluate some measure that can compute the relevance of an attribute regarding a given class or concept. Such measures involve information gain, ambiguity, and correlation coefficient.
Attribute relevance analysis for concept description is implemented as follows −
Data collection − It can collect data for both the target class and the contrasting class by query processing.
Preliminary relevance analysis using conservative AOI − This step recognizes a set of dimensions and attributes on which the selected relevance measure is to be used.
AOI can be used to implement preliminary analysis on the data by eliminating attributes having a high number of distinct values. It can be conservative, the AOI implemented should employ attribute generalization thresholds that are set reasonably large to enable more attributes to be treated in further relevance analysis by the selected measure.
Remove − This process removes irrelevant and weakly relevant attributes using the selected relevance analysis measure.
Generate the concept description using AOI − It can implement AOI using a less conservative set of attribute generalization thresholds. If the descriptive mining function is class characterization, only the original target class working relation is included now.
If the descriptive mining function is class characterization, only the original target class working relation is included. If the descriptive mining function is class characterization, only the original target class working relation is included. If the descriptive mining function is class comparison, both the original target class working relation and the original contrasting class working relation are included.
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