Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Web usage mining to improve the design of an e-commerce website: OrOliveSur.com
Expert Systems with Applications: An International Journal
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When we deal with big repositories to extract relevant information in a short period of time, pattern extraction using data mining can be employed. One of the most used patterns employed are Association Rules, which can measure item co-occurrence inside large set of transactions. We have discovered a certain type of transactions that can be employed more efficiently that have been used until today. In this work we have applied a new methodology to this type of transactions, and thus we have obtained execution times much faster and more information than that obtained with classical algorithms of Association Rule Mining. In this way we are trying to improve the response time of a recommendation web system in order to offer better responses to our users in less time.