Data Mining functionalities

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Data mining functions are used to define the kind of patterns that will be discovered during data mining jobs.

Some of the major data mining functionalities are as follows: 

Class/ concept descriptions: Characterization and Discrimination

  • Class/concept descriptions are the definitions of a class or idea.
  • Data features should be generalised, summarised, and contrasted.
  • For example, at the Electronics shop, computer and printer classes of things are for sale, and client ideas include large spenders and budget spenders.
  • Data Characteristics: The characterization of data is a description of the key characteristics of objects in a target class which create what is called a characteristics rule.
  • Data Discrimination: It compares common feature of class which is under study. The output of this process can be representation many forms.

Mining frequent patterns, Association rules and Correlations

  • Patterns that appear frequently in data are known as frequent patterns.
  • Mining frequent patterns leads to the discovery of interesting associations and correlations within data. 
TIDItems
1Milk, Bread, Cigarette
2Milk, Bread, Sugar
3Milk, Bread, Pen
  • Here,  frequent pattern is { milk, bread} 
  •  Association rule is milk-> bread  If the sale of milk is increased then the sale of bread also increases this indicates correlation.

Classification and Regression for Predictive analysis 

  • The process of finding a model that explains and separates data classes or ideas is known as classification.
  • It’s used to figure out what class an object belongs to when the class label isn’t known.
  • Describe and distinguish classes or concepts for future prediction
  • E.g., classify people based on age, income, etc.
Data Mining functionalities
  • Continuous-valued functions are used in the prediction model.
  • It is used to predict missing or unavailable numerical data values rather than class labels.
Data Mining functionalities

 Cluster Analysis for clustering 

  • Clustering groups data to form new clusters, e.g., cluster fruits to find distribution patterns.
  • It can used to generate such labels.
  • The objects are grouped based on the principle of maximising the intra-class similarity and minimising the intra class similarity.

Data Mining functionalities

 Outlier Analysis

  • A data object that does not comply with the general behaviour of the data is called outlier.
  • Outliers may be detected using statistical tests that assume a distribution or probability model for the data,or using distance measures .
  • Useful in fraud detection, rare events analysis.

Data Mining functionalities

Also Read: Introduction To Data Mining

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