Generating non-redundant association rules

By Mohammed J. Zaki. Knowledge Discovery and Data Mining, 2000

572 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0307522ebb388187e7e7ae1530349bcca206cbb3

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In Computational Theory and Mathematics

In Information Systems

In Signal Processing

Citations in the corpus

Alternative Interest Measures for Mining Associations in Databases (2003, background): “In particular, finding associations between items in a database of customer transactions, such as the sales data collected at super market check out counters [3], [5], [11], [13], [14], [17], [20], [25], [26], [27], [28] has become an important data mining task.”
Efficient algorithms for mining closed itemsets and their lattice structure (2005, background): “The second is to mine only the frequent closed sets [3], [16], [17], [20], [21].”
CLOSET+: searching for the best strategies for mining frequent closed itemsets (2003, background): “Mining closed itemsets, as shown in [17], can lead to orders of magnitude smaller result set (than mining frequent itemsets) while retaining the completeness, i.”
Discovering data quality rules (2008, methodology): “Although there are techniques for pruning and aggregating association rules, such as using closed frequent itemsets [25], seeking highly correlated itemsets [8], and dimension hierarchies, these techniques can still return a larger number of rules than our method.”
Fast vertical mining using diffsets (2003, methodology): “use a vertical database format, which include Eclat [22], Charm [21], and Partition [18].”
H-mine: hyper-structure mining of frequent patterns in large databases (2001, background): “As an important data mining problem, frequent pattern mining plays an essential role in many data mining tasks, such as mining associations [3, 8], sequential patterns [15, 12], max-patterns and frequent closed patterns [4, 11, 17 ], classification [7, 16], and clustering [2].”
Efficiently mining maximal frequent itemsets (2001, background): “Another recent promising direction is to mine only closed sets [9, 11]; a set is closed if it has no superset with the same frequency.”
CHARM: An Efficient Algorithm for Closed Itemset Mining (2002, methodology): “Related Work Our past work [18] addressed the problem of non-redundant rule generation, provided that closed sets are available; an algorithm to efficiently mine the closed sets was not described in that paper.”