An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is “spam” or “non-spam”). This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns.

Rhythm—The term “rhythm” has more than one meaning. It can mean the basic, repetitive pulse of the music, or a rhythmic pattern that is repeated throughout the music (as in “feel the rhythm”). It can also refer to the pattern in time of a single small group of notes (as in “play this rhythm for me”).

Rhythm is the regular movement of anything through time, and melody is specifically a succession of tones set to a rhythm.

The patterns are similarities or characteristics that some of the problems share. Pattern recognition is one of the four cornerstones of Computer Science. It involves finding the similarities or patterns among small, decomposed problems that can help us solve more complex problems more efficiently.

Compared to all mental abilities, pattern recognition is said to have the highest correlation with the so-called general intelligence factor (Kurzweil, 2012). Because the brain is wired to recognize patterns, everyone has the potential to be pattern smart, but, in different ways.

Seeing familiar objects or patterns in otherwise random or unrelated objects or patterns is called pareidolia. It’s a form of apophenia, which is a more general term for the human tendency to seek patterns in random information.

Pareidolia (/pɛraɪˈdoʊliə/, /pɛriː-/) is the tendency for incorrect perception of a stimulus as an object, pattern or meaning known to the observer. Common examples are perceived images of animals, faces, or objects in cloud formations, or lunar pareidolia like the Man in the Moon or the Moon Rabbit.