Alternative interest measures for mining associations in databases

Abstract

Data mining is defined as the process of discovering significant and potentially useful patterns in large volumes of data. Discovering associations between items in a large database is one such data mining activity. In finding associations, support is used as an indicator as to whether an association is interesting. In this paper, we discuss three alternative interest measures for associations: any-confidence, all-confidence, and bond. We prove that the important downward closure property applies to both all-confidence and bond. We show that downward closure does not hold for any-confidence. We also prove that, if associations have a minimum allconfidence or minimum bond, then those associations will have a given lower bound on their minimum support and the rules produced from those associations will have a given lower bound on their minimum confidence as well. However, associations that have that minimum support (and likewise their rules that have minimum confidence) may not satisfy the minimum all-confidence or minimum bond constraint. We describe the algorithms that efficiently find all associations with a minimum all-confidence or minimum bond and present some experimental results.

arules- A Computational Environment for Mining Association Rules and Frequent Item Sets

“…In this example, we show how easy it is to add a new interest measure, using all-confidence as introduced by Omiecinski (2003). The all-confidence of an itemset X is defined as all-confidence(X) = supp(X) max I⊂X supp(I)…”

“…Frequent closed itemsets are a superset of the maximal frequent itemsets. Their advantage over maximal frequent itemsets is that in addition to yielding all frequent itemsets, they also preserve the support information for all frequent itemsets which can be important for computing additional interest measures after the mining process is finished (e.g., confidence for rules generated from the found itemsets, or all-confidence (Omiecinski 2003)). …”

Mining frequent arrangements of temporal intervals

“…Based on the traditional definition of all-confidence (Omiecinski, 2003), the denominator is the maximum number of e-sequences in D that contain any sub-arrangement of A and t is the size of arrangement A. This states that all-confidence is in fact the smallest confidence of any rule inferred from A.…”

“…(Omiecinski, 2003) proposes alternative association rule measures for evaluating the importance of association rules in transactional databases, whereas (Kamber & Shinghal, 1996) introduces some efficient techniques for evaluating the interestingness of rules. An alternative definition of confidence for error-tolerant itemsets and continuous data is described in (Steinbach, et al, 2007).…”

“…There have been several works on bounding interestingness measures for frequent itemset mining (Bayardo et al, 1999;Webb & Zhang, 2005;Omiecinski, 2003). These bounds were used to prune the search space during the rule extraction process.…”