Data mining methods

 Data mining methods  

Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times

The most popular types of data mining techniques include association rules, classification, clustering,neural networks and predictive analysis. Association rules, also referred to as market basket analysis, search for relationships between variables. 

Association rule - The association rule refers to the if-then statements that establish correlations and relationships between two or more data items. The correlations are evaluated using support and confidence metrics, wherein support determines the frequency of occurrence of data items within the dataset. 

Classification- The classification data mining technique classifies data items within a dataset into different categories. For example, we can classify vehicles into different categories, such as sedan, hatchback, petrol, diesel, electric vehicle, etc., based on attributes such as the vehicle’s shape, wheel type, or even number of seats. When a new vehicle arrives, we can categorize it into various classes depending on the identified vehicle attributes. One can apply the same classification strategy to classify customers based on their age, address, purchase history, and social group. 

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