arules (version 0.4-2)

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, ...)

Arguments

x

a set of itemsets or rules.

method

name of the interest measure (see details for available measures).

transactions

the transaction data set used to mine the associations.

...

further arguments for the measure calculation.

Value

• A numeric vector containing the values of the interest measure for each association in the set of associations x.

Details

For itemsets the following measures are implemented: [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]

Note that for calculating the interest measures support (for rules also confidence and lift) stored in the quality slot of x are needed. These measures are returned by the mining algorithms implemented in this package. Note also, that the calculation of some measures is quite slow since we do not have access to the original itemset structure which was used for mining.

References

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, Kurt Hornik, and Thomas Reutterer (2005). Implications of probabilistic data modeling for rule mining. Report 14, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversit�t Wien, Augasse 2-6, 1090 Wien, Austria.

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.

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.

See Also

itemsets-class, rules-class

Examples

data("Income")
rules <- apriori(Income)

quality(rules) <- cbind(quality(rules), 
	hyperConfidence = interestMeasure(rules, method = "hyperConfidence", 
	Income))

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