Pattern Discovery in Data Mining

- Overview

Pattern discovery in data mining is the process of finding interesting patterns in temporal data. These patterns can be periodic, abnormal, or sequential. 

Pattern discovery is one of the most important data-mining tasks and can be applied to many domains. It can help improve the quality and efficiency of insights, and help people better understand data. 

Here are some examples of pattern discovery:

• Sequential patterns: For example, sequences of errors or warnings that precede an equipment failure may be used to schedule preventative maintenance or may provide insight into a design flaw.
• Substructure discovery: A set of items, subsequences, or substructures that occur frequently together (or strongly correlated) in a data set.

Some popular measures for pattern discovery include leverage and lift. 

One data mining technique is clustering, which groups data points into clusters based on their similarity. This can be a powerful tool for uncovering patterns and relationships in large datasets.

[More to come ...]