Research on Association Rule Mining

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Definition

The problem of mining association rules (see association rule mining at Wikipedia)was introduced in Agrawal et at. 1993 (see the annotated bibliography). The aim of association rule mining is to find interesting and useful patterns in a transaction database. The database contains transactions which consist of a set of items and a transaction identifier (e.g., a market basket). Association rules are implications of the form X -> Y where X and Y are two disjoint subsets of all available items. X is called the antecedent or LHS (left hand side) and Y is called the consequent or RHS (right hand side). Association rules have to satisfy constraints on measures of significance and interestingness (see commonly used measures of interestingness).

Data Sets

UCI KDD Archive, an online repository of large data sets which encompasses a wide variety of data types, analysis tasks, and application areas.
Traces available in the Internet Traffic Archive. Data sets with packet traces, HTTP logs and more.
KDD Cup Data, data sets and results for the annual Data Mining and Knowledge Discovery competition organized by ACM Special Interest Group on Knowledge Discovery and Data Mining.
FIMI Dataset Repository

Events

My Implementations

arules, a package for mining association rules and frequent itemsets with R. It provides an easy to use and flexible platform for experiments and research.
NBMiner: Implementation of the mining algorithm and estimation procedure developed in Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2):137-166, September 2006. You can download arulesNBMiner (add-on to arules).

Other Implementations

Boergelt's implementation of Apriori and Eclat
Frequent pattern mining implementations from Bart Goethals
Data Mining Software by Mohammed J. Zaki
Frequent Itemset Mining Implementations Repository (FIMI)
Weka, a collection of machine learning algorithms for data mining tasks written in Java.
KDnuggets, Gregory Piatetsky-Shapiro's Web portal for Data Mining, Knowledge Discovery, Text Mining, and Web Mining.
The Data Mine A Wiki-style portal for Data Mining.

Journals

ACM Transactions on Knowledge Discovery from Data (TKDD) - ACM
Data Mining and Knowledge Discovery (DAMI) - Springer
Data & Knowledge Engineering (DKE) - Elsevir
Statistical Analysis and Data Mining - Wiley
Knowledge and Information Systems: An International Journal - Springer
IEEE Transactions on Knowledge and Data Engineering (TKDE) - IEEE
International Journal of Data Warehousing and Mining (IJDWM) - IGI Publishing
International Journal of Business Intelligence and Data Mining (IJBIDM) - Inderscience
International Journal of Information Technology & Decision Making (IJITDM) - World Scientific
Intelligent Data Analysis: An International Journal - IOS Press
Journal of Database Management (JDM) - Idea Group
SIGKDD Explorations - ACM
INFORMS Journal on Computing (JOC) - informs
Journal of Intelligent Information Systems - Springer
Machine Learning - Springer
Journal of Machine Learning Research
Transactions on Machine Learning and Data Mining - ibai Publishing

My Publications

[1]	Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303-315, April 2008. [ DOI \| at the publisher \| .pdf ]
[2]	Michael Hahsler and Kurt Hornik. New probabilistic interest measures for association rules. Intelligent Data Analysis, 11(5):437-455, 2007. [ at the publisher \| .pdf ]
[3]	Thomas Reutterer, Michael Hahsler, and Kurt Hornik. Data Mining und Marketing am Beispiel der explorativen Warenkorbanalyse. Marketing ZFP, 29(3):165-181, 2007. [ at the publisher ]
[4]	Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2):137-166, September 2006. [ DOI \| at the publisher \| .pdf ]
[5]	Michael Hahsler and Kurt Hornik. New probabilistic interest measures for association rules. Report 38, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2-6, 1090 Wien, Austria, August 2006. [ at the publisher ]
[6]	Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Magdeburg, March 9-11, 2005, Studies in Classification, Data Analysis, and Knowledge Organization, pages 598-605. Springer-Verlag, 2006. [ at the publisher \| .pdf ]
[7]	Michael Hahsler, Bettina Grün, and Kurt Hornik. arules - A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15):1-25, October 2005. [ at the publisher \| .pdf ]
[8]	Michael Hahsler, Bettina Grün, and Kurt Hornik. A computational environment for mining association rules and frequent item sets. Report 15, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2-6, 1090 Wien, Austria, April 2005. [ at the publisher ]
[9]	Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Implications of probabilistic data modeling for rule mining. Report 14, Research Report Series, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2-6, 1090 Wien, Austria, March 2005. [ at the publisher ]
[10]	Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Working Paper 07/2004, Working Papers on Information Processing and Information Management, Institut für Informationsverarbeitung und -wirtschaft, Wirtschaftsuniversität Wien, Augasse 2-6, 1090 Wien, Austria, November 2004. [ at the publisher ]
[11]	Andreas Geyer-Schulz, Michael Hahsler, and Anke Thede. Comparing association-rules and repeat-buying based recommender systems in a B2B environment. In M. Schader, W. Gaul, and M. Vichi, editors, Between Data Science and Applied Data Analysis, Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22-24, 2002, Studies in Classification, Data Analysis, and Knowledge Organization, pages 421-429. Springer-Verlag, July 2003. [ at the publisher ]
[12]	Andreas Geyer-Schulz and Michael Hahsler. Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In Brij Masand, Myra Spiliopoulou, Jaideep Srivastava, and Osmar R. Zaiane, editors, Fourth WEBKDD Workshop: Web Mining for Usage Patterns & User Profiles, pages 100-114, Edmonton, Canada, July 2002. [ .pdf ]