Association Rule Mining Algorithms for Set-Valued Data Export

@incollection{citeulike:5956826, abstract = {This paper presents an association rule mining system that is capable of handling set-valued attributes. Our previous research has exposed us to a variety of real-world biological datasets that contain attributes whose values are sets of elements, instead of just individual elements. However, very few data mining tools accept datasets that contain these set-valued attributes, and none of them allow the mining of association rules directly from this type of data. We introduce in this paper two algorithms for mining (classification) association rules directly from set-valued data and compare their performance. We have implemented a system based on one of these algorithms and have applied it to a number of biological datasets. We describe here our system and highlight its merits by means of comparing the results achieved with it and the failed attempts to mine association rules from those datasets using standard tools. Our system makes the creation of input files containing set-valued data much easier, and makes the mining of association rules directly from these data possible.}, author = {Shoemaker, Christopher and Ruiz, Carolina}, citeulike-article-id = {5956826}, citeulike-linkout-0 = {http://www.springerlink.com/content/nt6mb8x3yekhxa8t}, journal = {Intelligent Data Engineering and Automated Learning}, keywords = {algorithm, mining\_system}, pages = {669--676}, posted-at = {2009-10-17 19:41:14}, priority = {2}, title = {Association Rule Mining Algorithms for Set-Valued Data}, url = {http://www.springerlink.com/content/nt6mb8x3yekhxa8t}, year = {2003} }

TY - CHAP ID - citeulike:5956826 L3 - citeulike-article-id:5956826 N2 - This paper presents an association rule mining system that is capable of handling set-valued attributes. Our previous research has exposed us to a variety of real-world biological datasets that contain attributes whose values are sets of elements, instead of just individual elements. However, very few data mining tools accept datasets that contain these set-valued attributes, and none of them allow the mining of association rules directly from this type of data. We introduce in this paper two algorithms for mining (classification) association rules directly from set-valued data and compare their performance. We have implemented a system based on one of these algorithms and have applied it to a number of biological datasets. We describe here our system and highlight its merits by means of comparing the results achieved with it and the failed attempts to mine association rules from those datasets using standard tools. Our system makes the creation of input files containing set-valued data much easier, and makes the mining of association rules directly from these data possible. JF - Intelligent Data Engineering and Automated Learning EP - 676 TI - Association Rule Mining Algorithms for Set-Valued Data SP - 669 KW - algorithm KW - mining_system AU - Shoemaker, Christopher AU - Ruiz, Carolina PY - 2003/// UR - http://www.springerlink.com/content/nt6mb8x3yekhxa8t ER -