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[238] by Brin et al. Because of the limitation of confidence, Brin et al. [238] had proposed the idea of using interest factor as a measure of interesting- ness. The all-confidence measure was proposed by Omiecinski [289]. Xiong et al. [330] introduced the cross-support property and showed that the all- confi.dence measure can be used to eliminate cross-support patterns. A key difficulty in using alternative objective measures besides support is their lack of a monotonicity property, which makes it difficult to incorporate the mea- sures directly into the mining algorithms. Xiong et al. [328] have proposed an efficient method for mining correlations by introducing an upper bound function to the fcoefficient. Although the measure is non-monotone, it has an upper bound expressign that can be exploited for the efficient mining of strongly correlated itempairs.

Fabris and Fleitas [249] have proposed a method for discovering inter- esting associations by detecting the occurrences of Simpson’s paradox [309]. Megiddo and Srikant [282] described an approach for validating the extracted

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396 Chapter 6 Association Analysis

patterns using hypothesis testing methods. A resampling-based technique was also developed to avoid generating spurious patterns because of the multiple comparison problem. Bolton et al. [237] have applied the Benjamini-Hochberg

[236] and Bonferroni correction methods to adjust the p-values of discovered patterns in market basket data. Alternative methods for handling the multiple comparison problem were suggested by Webb [326] and Zhang et al. [338].

Application of subjective measures to association analysis has been inves- tigated by many authors. Silberschatz and Tuzhilin [307] presented two prin-

ciples in which a rule can be considered interesting from a subjective point of view. The concept of unexpected condition rules was introduced by Liu et al. in 12771. Cooley et al. [243] analyzed the idea of combining soft belief sets using the Dempster-Shafer theory and applied this approach to identify contra- dictory and novel association patterns in Web data. Alternative approaches include using Bayesian networks [269] and neighborhood-based information

[2a5] to identify subjectively interesting patterns. Visualization also helps the user to quickly grasp the underlying struc-

ture of the discovered patterns. Many commercial data mining tools display the complete set of rules (which satisfy both support and confidence thresh- old criteria) as a two-dimensional plot, with each axis corresponding to the antecedent or consequent itemsets of the rule. Hofmann et al. [263] proposed using Mosaic plots and Double Decker plots to visualize association rules. This approach can visualize not only a particular rule, but also the overall contin- gency table between itemsets in the antecedent and consequent parts of the rule. Nevertheless, this technique assumes that the rule consequent consists of only a single attribute.

Application fssues

Association analysis has been applied to a variety of application domains such as Web mining 1296,3L71, document analysis 1264], telecommunication alarm diagnosis [271], network intrusion detection 1232,244,275], and bioinformatics

1302, 3271. Applications of association and correlation pattern analysis to Earth Science studies have been investigated in [298, 299, 319].

Association patterns have also been applied to other learning problems such as classification1276,278], regression [291], and clustering1257,329,332]. A comparison between classification and association rule mining was made by Freitas in his position paper [251]. The use of association patterns for clustering has been studied by many authors including Han et al.l257l, Kosters et al. 12721, Yang et al. [332] and Xiong et al. [329].



Bibliography 397

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398 Chapter 6 Association Analysis

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