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The Apriori algorithm is an algorithm that attempts to operate on database records, particularly transactional records, or records including certain numbers of fields or items. It is one of a number of algorithms using a "bottom-up approach" to incrementally contrast complex records, and it is useful in today's complex machine learning and artificial intelligence projects.
Essentially, the Apriori algorithm takes each part of a larger data set and "scores" it or contrasts it with other sets in some ordered way. The resulting scores are used to generate sets that are classed as frequent appearances in a larger database for aggregated data collection.
In a practical sense, one can get a better idea of the algorithm by looking at applications such as a "market basket tool" that helps with figuring out which items are purchased together in a market basket, or a financial analysis tool that helps to show how various stocks trend together. Scientists, on the other hand, can get a better description of the Apriori algorithm from its pseudocode, which is widely available online.
The Apriori algorithm may be used in conjunction with other algorithms to effectively sort and contrast data to show a much better picture of how complex systems reflect patterns and trends.