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Pattern Detection

This page is about Pattern Detection. For Pattern Recognition, see Pattern Recognition (disambiguation).

In Analytics, 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.

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 clustered entities, allowing ranking algorithms to quantify the patterns in the graph.

Disadvantages

Pattern detection analyzes numerical and text data, making the approach ineffective for images, audio, and video, where machine learning and deep learning techniques are required.