interestMeasure: Calculating various additional interest measures

Description

Provides the generic function interestMeasure and the needed S4 method to calculate various additional interest measures for existing sets of itemsets or rules.

Usage

interestMeasure(x, method, transactions = NULL, reuse = TRUE, ...)

Arguments

a set of itemsets or rules.

method

name or vector of names of the desired interest measures (see details for available measures).

transactions

the transaction data set used to mine the associations.

reuse

logical indicating if information in quality slot should be reuse for calculating the measures. This speeds up the process significantly since only very little (or no) transaction counting is necessary if support, confidence and lift ar

...

further arguments for the measure calculation.

Details

For itemsets the following measures are implemented: [object Object],[object Object],[object Object],[object Object]

For rules the following measures are implemented: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] If only one method is used, the function returns a numeric vector containing the values of the interest measure for each association in the set of associations x.

If more than one methods are specified, the result is a data.frame containing the different measures for each association. R. Bayardo, R. Agrawal, and D. Gunopulos (2000). Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery, 4(2/3):217--240, 2000.

Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, and Shalom Tsur (1997). Dynamic itemset counting and implication rules for market basket data. In SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, pages 255--264, Tucson, Arizona, USA.

Michael Hahsler and Kurt Hornik. New probabilistic interest measures for association rules. Intelligent Data Analysis, 11(5):437--455, 2007

Heike Hofmann and Adalbert Wilhelm. Visual comparison of association rules. Computational Statistics, 16(3):399--415, 2001.

Ron Kenett and Silvia Salini. Relative Linkage Disequilibrium: A New measure for association rules. In 8th Industrial Conference on Data Mining ICDM 2008 July 16--18, 2008, Leipzig/Germany, to appear, 2008.

Bing Liu, Wynne Hsu, and Yiming Ma (1999). Pruning and summarizing the discovered associations. In KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 125--134. ACM Press, 1999.

Edward R. Omiecinski (2003). Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering, 15(1):57--69, Jan/Feb 2003.

Pang-Ning Tan, Vipin Kumar, and Jaideep Srivastava (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4):293--313.

Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pages 229--248.

Hui Xiong, Pang-Ning Tan, and Vipin Kumar (2003). Mining strong affinity association patterns in data sets with skewed support distribution. In Bart Goethals and Mohammed J. Zaki, editors, Proceedings of the IEEE International Conference on Data Mining, November 19--22, 2003, Melbourne, Florida, pages 387--394.

itemsets-class, rules-class data("Income") rules <- apriori(Income)

## calculate a single measure and add it to the quality slot quality(rules) <- cbind(quality(rules), hyperConfidence = interestMeasure(rules, method = "hyperConfidence", Income))

inspect(head(sort(rules, by = "hyperConfidence")))

## calculate several measures m <- interestMeasure(rules, c("confidence", "oddsRatio", "leverage"), Income) inspect(head(rules)) head(m) models