ENBIS-8 in Athens

21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008

On Applications of the Relative Linkage Disequilibrium

23 September 2008, 15:00 – 15:20


Submitted by
Ron S. Kenett and Silvia Salini
KPA Ltd., Raanana, Israel and University of Torino, Torino, Italy, email: ron@kpa.co.il, Department of Economics, Business and Statistics. University
Relative Linkage Disequilibrium (RLD) was originally proposed as an approach to analyse both quantitatively and graphically general two way contingency tables (Kenett 1983). It was later expanded to the Data Mining context to evaluate Association Rules (Kenett and Salini, 2008). RLD can be interpreted graphically using a simplex representation leading to powerful graphical display of association relationships. Moreover the statistical properties of RLD are known so that confirmatory statistical tests of significance or basic confidence intervals can be applied. In this work we present several applications of RLD such as Risk Management, Kansei Engineering, Text mining, web clickstream analysis.

(Some) References

Hahsler, M., Gr¨un, B., and Hornik, K. (2005). arules – A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15):1–25. ISSN 1548-7660. URL http://www.jstatsoft.org/v14/i15/.

Kenett, R. (1983). On an Exploratory Analysis of Contingency Tables. The Statistician, 32, pp. 395-403.

Kenett, R and Salini, S., "Relative Linkage Disequilibrium: A New measure for association rules" (March 2008). UNIMI - Research Papers in Economics, Business, and Statistics. Statistics and Mathematics. Working Paper 32. 

Multi-Industry Semantic-Based Business Intelligence Solutions (2008), http://www.musing.eu/download-area/musing-public-documentation/it-operational-risk-bi-system-executive-summary-mar-2007-v1-0-kpa/at_download/file

Nagamachi, M. (1995), Kansei Engineering: a new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15: 3-11

Omiecinski, E. (2003). Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering, 15(1):57–69.

Shimada, K., Hirasawa K, and Hu J. (2006) Association Rule Mining with Chi-Squared Test Using Alternate Genetic Network Programming, ICDM2006.

Van Lottum, C., Pearce, K., Coleman, S. (2006), Features of Kansei Engineering Characterizing its Use in Two Studies: Men’s Everyday Footwear and Historic Footwear. Quality and Reliability Engineering International, 22: 629-650.
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