Data Mining Patterns

Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. 

The term "data mining" is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.

Useful Article

(Comprehensive coverage of topic)

https://www.tutorialspoint.com/data_mining/dm_tasks.htm

Mining of Frequent Patterns

Frequent patterns are those patterns that occur frequently in transactional data. Here is the list of kind of frequent patterns −

• Frequent Item Set − It refers to a set of items that frequently appear together, for example, milk and bread.

• Frequent Subsequence − A sequence of patterns that occur frequently such as purchasing a camera is followed by memory card.

• Frequent Sub Structure − Substructure refers to different structural forms, such as graphs, trees, or lattices, which may be combined with item-sets or subsequences.

What kind of knowledge can be mined?

• Characterization.
• Discrimination.
• Association and Correlation Analysis.
• Classification.
• Prediction.
• Clustering.
• Outlier Analysis.
• Evolution Analysis.

Practical article with useful information

https://www.kdnuggets.com/2016/12/data-science-basics-types-patterns-mined-data.html