Introduction to arules — Mining Association Rules and Frequent Item Sets

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

[1]  Mohammed J. Zaki.  Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[2]  Kurt Hornik,et al.  Selective association rule generation , 2008, Comput. Stat..

[3]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[4]  Edward Omiecinski,et al.  Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..

[5]  Kurt Hornik,et al.  New probabilistic interest measures for association rules , 2007, Intell. Data Anal..

[6]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[7]  Mohammed J. Zaki.  Mining Non-Redundant Association Rules , 2004, Data Min. Knowl. Discov..

[8]  Jaideep Srivastava,et al.  Selecting the right objective measure for association analysis , 2004, Inf. Syst..

[9]  M. Schwarz,et al.  Otto-von-Guericke-University of Magdeburg , 2007 .

[10]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[11]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

[12]  Kurt Hornik,et al.  Implications of probabilistic data modeling for rule mining , 2005 .

[13]  Bart Goethals,et al.  Advances in frequent itemset mining implementations: report on FIMI'03 , 2004, SKDD.

[14]  Rajeev Motwani,et al.  Beyond Market Baskets: Generalizing Association Rules to Dependence Rules , 1998, Data Mining and Knowledge Discovery.

[15]  Wynne Hsu,et al.  Pruning and summarizing the discovered associations , 1999, KDD '99.

[16]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[17]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[18]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[19]  Christian Borgelt,et al.  Induction of Association Rules: Apriori Implementation , 2002, COMPSTAT.

[20]  Kurt Hornik,et al.  Building on the Arules Infrastructure for Analyzing Transaction Data with R , 2006, GfKl.

[21]  Joydeep Ghosh,et al.  Distance based clustering of association rules , 1999 .

[22]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[23]  Srinivasan Parthasarathy,et al.  Efficient progressive sampling for association rules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[24]  Christian Borgelt,et al.  EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT , 2003 .

[25]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[26]  Philip S. Yu,et al.  Finding Localized Associations in Market Basket Data , 2002, IEEE Trans. Knowl. Data Eng..

[27]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[28]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[29]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[30]  William DuMouchel,et al.  Empirical bayes screening for multi-item associations , 2001, KDD '01.

[31]  Joydeep Ghosh,et al.  Relationship-Based Clustering and Visualization for High-Dimensional Data Mining , 2003, INFORMS J. Comput..

[32]  Dimitrios Gunopulos,et al.  Constraint-Based Rule Mining in Large, Dense Databases , 2004, Data Mining and Knowledge Discovery.

[33]  Donald E. Knuth,et al.  The Art of Computer Programming: Volume 3: Sorting and Searching , 1998 .

[34]  Srinivasan Parthasarathy,et al.  Evaluation of sampling for data mining of association rules , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[35]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[36]  HornikKurt,et al.  New probabilistic interest measures for association rules , 2007 .

[37]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[38]  Donald E. Knuth,et al.  The art of computer programming: sorting and searching (volume 3) , 1973 .

[39]  Kurt Hornik,et al.  Introduction to arules – A computational environment for mining association rules and frequent item sets , 2009 .

[40]  Hui Xiong,et al.  Mining strong affinity association patterns in data sets with skewed support distribution , 2003, Third IEEE International Conference on Data Mining.