Mining frequent patterns with counting inference

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How researchers see this paper

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Mining All Non-derivable Frequent Itemsets (2002, methodology): “Then there is no need to explicitly count the support of I requiring a complete database scan; if we need the support of I , we can always simply derive it using the deduction rules.”
Association mining (2006, methodology): “Counting Inference Counting inference [Bastide et al. 2000] (implemented as PASCAL, an Apriori extension) is based on the observation that itemsets can be considered equivalent if they are included in the same set of objects, referred to as a class.”
Computing iceberg concept lattices with T (2002, methodology): “This is used for the PASCAL algorithm [5] which is related to TITANIC, and which efficiently computes frequent itemsets.”
CHARM: An Efficient Algorithm for Closed Itemset Mining (2002, methodology): “We compare CHARM against previous methods for mining closed sets such as Close [14], Closet [15], Mafia [6] and Pascal [3].”
Frequent item set mining (2012, methodology): “Additional pruning techniques for closed item sets as well as special algorithms have been suggested, for example, in Refs 19, 20, 22, 23, 37, 46–48.”
Efficient algorithms for mining closed itemsets and their lattice structure (2005, background): “The power of the equivalence class approach is that it breaks the original search space into independent subproblems.”