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pattern detection
In Analytics and Operations Research, Pattern Detection includes a number of methods for extracting meaning from large and complex data sets through a combination of operations research methods, graph theory, data analysis, clustering, and advanced mathematics. Unlike machine learning, deep learning, or data mining, pattern detection is data agnostic, requiring only an ingestible data format to compute correlations in data.
Graph algorithms detect patterns of co-occurrence to create a holistic representations of connections a given set of data. Analysis has been applied to industries including transportation, manufacturing, and others.
==Advantages==
The pattern detection approach to data analysis requires creating a graph representation of every connection in a given data set. The complex graph structure carries the relationships of co-occurring entities, allowing ranking algorithms to quantify the patterns in the graph.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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