Building and maintaining analysis-level class hierarchies using Galois Lattices
OOPSLA '93 Proceedings of the eighth annual conference on Object-oriented programming systems, languages, and applications
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discrete Applied Mathematics - Special issue: The 1998 conference on ordinal and symbolic data analysis (OSDA '98)
CMRules: Mining sequential rules common to several sequences
Knowledge-Based Systems
Information Sciences: an International Journal
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Given a sample of binary random vectors with i.i.d. Bernoulli(p) components, that is equal to 1 (resp. 0) with probability p (resp. 1-p), we first establish a formula for the mean of the size of the random Galois lattice built from this sample, and a more complex one for its variance. Then, noticing that closed @a-frequent itemsets are in bijection with closed @a-winning coalitions, we establish similar formulas for the mean and the variance of the number of closed @a-frequent itemsets. This can be interesting for the study of the complexity of some data mining problems such as association rule mining, sequential pattern mining and classification.