Data mining methods

                                                            Data mining methods



Data mining is one of the fundamental Big Data analytics techniques. It is used to exploit interest patterns and uncover the concealed insights in large datasets. The different functions it performs like predicting outcomes, dividing data into clusters, and showcasing hidden relationships provide data mining with a wide range of business and research applications.

There are many data mining techniques, each performing a particular analysis based on the problem it is used to solve. Classification is among the most-widely used methods. It includes teaching models to categorize emails into specific folders like "spam" or "not spam." On the other hand, Clustering is an unsupervised approach by which data are grouped into similar clusters - it is applicable in customer segmentation or image processing. Association rule mining is aimed at finding ties between items that can be used in market basket analysis (e.g., those who buy bread often buy butter).

Regression is a popular method used for trend forecasting, e.g., sales predictions, or price predictions. Anomaly detection is another, it flags unwanted changes in patterns, such as line items in a fraudulent transaction. They are often used in combination in machine learning pipelines, thus, companies have access to a set of powerful tools that can enlighten and assist their decision-making process.


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