Mathematical Tools for Data Mining

  • Hahsler M
  • Grün B
  • Hornik K
  • et al.
ISSN: 1548-7660
N/ACitations
Citations of this article
88Readers
Mendeley users who have this article in their library.

Abstract

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 \pkgarules 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.

Cite

CITATION STYLE

APA

Hahsler, M., Grün, B., Hornik, K., & Buchta, C. (2008). Mathematical Tools for Data Mining. Journal of Statistical Software, 14(15), 1–25. Retrieved from http://www.jstatsoft.org/counter.php?id=140&url=v14/i15&ct=2; http://www.jstatsoft.org/counter.php?id=140&url=v14/i15/v14i15.pdf&ct=1%5Cnhttp://link.springer.com/10.1007/978-1-84800-201-2

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free