The Apriori algorithm finds frequent itemsets in a transactional dataset, such as items commonly bought together, and derives association rules from them, using the observation that any subset of a frequent itemset must itself be frequent to prune the search space: it builds up candidate itemsets one size at a time, discarding any candidate that contains an infrequent subset before counting its support in the data. Rakesh Agrawal and Ramakrishna Srikant published the algorithm in 1994 as an improvement on their own earlier AIS algorithm for market basket analysis. It remains a foundational method in data mining for association rule learning.
Facts
Time ComplexityO(2^|D|), exponential, where |D| is the horizontal width (total number of items) in the database 1 Connections
Invented By
Ramakrishnan Srikant co-developed the Apriori algorithm for mining frequent itemsets and association rules with Rakesh Agrawal, published in 1994.
Sources
1. Wikipedia: Apriori algorithm
Limitations
both the time and space complexity of this algorithm are very high
Overview
The Apriori algorithm was proposed by Agrawal and Srikant in 1994
View the SourceReader Challenges (0)
No disputes yet. Spotted an error or a better source? Open the first one.
Sign in to dispute this or suggest a correction.