About

A pattern means that the data (visual or not) are correlated that they have a relationship and that they are predictable.

When you have a lack of pattern, you have true randomness

When you find a pattern, you can have a good idea when or where something will happen before it actually happens.

See Data Mining - Signal (Wanted Variation)

Pattern detection is a goal of unsupervised learning

Beware of the human tendency bias to see patterns in random data.

See:

  • wiki/Apophenia: tendency to perceive meaningful connections between unrelated things
  • wiki/Pareidolia: tendency to interpret a vague stimulus as something known to the observer: seeing shapes in clouds, faces in inanimate objects, …

Example

Validation of the pattern

Further, the discovery of a particular pattern in a particular set of data does not necessarily mean that pattern is representative of the whole population from which that data was drawn. Hence, an important part of the process is the verification and validation of patterns on other samples of data.

…the curse of big data is the fact that when you search for patterns in very, very large data sets with billions or trillions of data points and thousands of metrics, you are bound to identify coincidences that have no predictive power.”

Documentation / Reference