To gain an understanding of statistical patterning, let us begin with thinking about what happens when an urban area is threatened by severe weather and potentially hazardous traveling—all the local stores sell out of bread, milk, and eggs!
Patterning (which is a subfield of data mining) is the process of looking through data in an effort to identify previously unknown but potentially useful patterns consisting of frequently co-occurring events (such as the stormy weather event triggering the sale of bread, milk, and eggs) or objects (such as the products bread, milk, and eggs being typically purchased together or bundled together in the same shopping cart).
Pattern mining is the process that consists of using or developing custom pattern mining logic. This logic might be applied to various types of data sources (such as transaction and sequence databases, streams, strings, spatial data, graphs, and so on) in an effort to look for various types of patterns.
At a higher level...