Which characteristic of computational thinking involves finding similarities and patterns within data?

The characteristic of computational thinking that involves finding similarities and patterns within data is pattern recognition. This process allows individuals to identify trends, relationships, or recurring themes in data sets, which can be fundamental in problem-solving, data analysis, and developing algorithms. By recognizing patterns, one can make predictions, generalizations, or derive insights that help in decision making and designing effective solutions.

Decomposition refers to breaking down complex problems into simpler, more manageable parts. While this is an essential step in the problem-solving process, it does not specifically focus on identifying similarities and patterns.

Abstraction involves generalizing information by stripping away unnecessary details to focus on the essential aspects of a problem. This is also critical in computational thinking but does not directly relate to recognizing patterns within data.

Algorithm design is the process of developing a set of steps or procedures to solve a particular problem. While algorithms may utilize patterns recognized during analysis, the act of finding those patterns is distinctly captured by the characteristic of pattern recognition.