Data mining methods

 Data mining is a technique used to process data and explore patterns in large data sets to develop practical and data-driven insights. The vital aspects of data mining include data cleaning, data transformation, and data integration.


A few data mining methods are down below


1. Pattern Tracking: Pattern tracking is one of the fundamental data mining techniques. It entails recognizing and monitoring trends in sets of data to make intelligent analyses regarding business outcomes. For a business, this process could relate to anything from identifying top-performing demographics or understanding seasonal variations in the customer’s buying behaviour.


2. Classification: It’s a useful data mining technique used to derive relevant data and metadata based on a defined attribute, for example, type of data sources, data mining functionalities, and more. Basically, it’s the process of dividing large datasets into target categories.


3. Clustering: Like classification, clustering is a data mining technique that involves grouping data based on similarities. It helps in knowledge discovery, anomaly detection, and gaining insights into the internal structure of the data.


4. Decision Tree: A decision tree is a data mining technique in machine learning
that focuses on input and output modelling relationships using if/then rules. With this approach, you can learn how the data inputs influence outputs. The trees are typically designed in a top-down, flowchart-like structure.

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