Last modified on July 26th, 2020
Analytical Characterization in Data Mining – Attribute Relevance Analysis
Analytical Characterization is a very important topic in data mining, and we will explain it with the following situation;
We want to characterize the class or in other words, we can say that suppose we want to compare the classes. Now the confusing question is that What if we are not sure which attribute we should include for the class characterization or class comparison? If we specify too many attributes, then these attributes can be a solid reason to slow down the overall process of data mining.
We can solve this problem with the help of analytical characterization.
Analytical characterization is used to help and identifying the weakly relevant, or irrelevant attributes. We can exclude these unwanted irrelevant attributes when we preparing our data for the mining.
Why Analytical Characterization?
Analytical Characterization is a very important activity in data mining due to the following reasons;
Due to the limitation of the OLAP tool about handling the complex objects.
Due to the lack of an automated generalization, we must explicitly tell the system which attributes are irrelevant and must be removed, and similarly, we must explicitly tell the system which attributes are relevant and must be included in the class characterization.
Attribute generalization thresholds
Due to the lack of an automated generalization, we must explicitly tell the system how much deeper we need to generalize the attribute.
The process of generalization is totally dependent on the user who explicitly performs all these actions.
How to Analyse Attribute Relevance?
The data is collected for the target class and its contrasting class.
Preliminary relevance analysis with the help of conservative AOI
We need to decide a set of dimensions and attributes and apply the selected relevance measure on them. The candidate relation of the mining task is a term used for obtaining the relation with such an application of Attribute Oriented Induction.
Relevance analysis to remove the irrelevant or weakly relevant attributes
This step consists of steps of Relevance analysis for removing the weakly or irrelevant attribute
Attribute Oriented Induction to generate the concepts
We need to perform the Attribute Oriented Induction. Attribute-Oriented Induction (AOI) is an algorithm for data summarization. AOI can suffer the problem of over-generalization. Data summarization is a data mining technique with the help of which we can summarize the big data in concise understandable knowledge.
We can determine the classifying power of an attribute within a set of data with the help of a Quantitative relevance measure.
Some competing methods of Relevance Measures are mentioned below;
- Gini index
- χ2 contingency table statistics
- Gain ratio (C4.5)
- Uncertainty coefficient
- information gain (ID3)
You must consult some important topics related to the analytical characterization that are mentioned below;
- analytical methods for physicochemical characterization of antibody-drug conjugates
- rti analytical scientist materials characterization base pay
- analytical characterization of proteins
- characterization of intrinsically disordered proteins by analytical ultracentrifugation
- analytical characterization of small molecules
- analytical question for characterization
- wear debris from brake system materials: a multi-analytical characterization approach
- unit d3.2 lipid crystal characterization current protocols in food analytical chemistry
- lockett modern analytical methods for separation and characterization
- rti analytical scientist materials characterization pay
- electrochemical characterization, electrochemical analytical modeling, process engineering.
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