Relevance shows how relevant a sub-pattern is with respect to either a pattern in your data or to all your data.
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You can think of Relevance as finding the 'uncommonly common' sub-pattern that occur more frequently with the pattern than they do in your overall data selection - and calculating a score that combines both the uniqueness factor and the frequency. 

You will often find that sub-patterns with a high Relevance will contain important descriptive and root cause information.