Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data Mining and Knowledge Discovery: Making Sense Out of Data
IEEE Expert: Intelligent Systems and Their Applications
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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Using concept lattices as a theoretical background for finding association rules [11] has led to designing algorithms like Charm [10], Close [7] or Closet [8]. While they are considered as extremely appropriate when finding concepts for association rules, due to the smaller amount of results, they do not cover a certain area of significant results, namely the pseudo-intents that form the base for global implications. We have proposed an approach that, besides finding all proper partial implications, also finds the pseudo-intents. The way our algorithm is devised, it allows certain important operations on concept lattices, like adding or extracting items, meaning we can reuse previously found results. It is a well-known fact that mining association rules may lead to a large amount of results. Since, the mining results are meant to be understood by the user, we have come to the conclusion that he will benefit more from starting small, with some of the items in the data base, understand a small amount of results, and then add items receiving only the extra-results. This way the number of human interventions during the "full" mining process is increased and the process becomes user-driven.