TY - JOUR
T1 - Association rules network
T2 - Definition and applications
AU - Pandey, Gaurav
AU - Chawla, Sanjay
AU - Poon, Simon
AU - Arunasalam, Bavani
AU - Davis, Joseph G.
PY - 2009/3
Y1 - 2009/3
N2 - The role of data mining is to search "the space of candidate hypotheses" to offer solutions, whereas the role of statistics is to validate the hypotheses offered by the data-mining process. In this paper we propose Association Rules Networks (ARNs) as a structure for synthesizing, pruning, and analyzing a collection of association rules to construct candidate hypotheses. From a knowledge discovery perspective, ARNs allow for a goal-centric, context-driven analysis of the output of association rules algorithms. From a mathematical perspective, ARNs are instances of backward-directed hypergraphs. Using two extensive case studies, we show how ARNs and statistical theory can be combined to generate and test hypotheses.
AB - The role of data mining is to search "the space of candidate hypotheses" to offer solutions, whereas the role of statistics is to validate the hypotheses offered by the data-mining process. In this paper we propose Association Rules Networks (ARNs) as a structure for synthesizing, pruning, and analyzing a collection of association rules to construct candidate hypotheses. From a knowledge discovery perspective, ARNs allow for a goal-centric, context-driven analysis of the output of association rules algorithms. From a mathematical perspective, ARNs are instances of backward-directed hypergraphs. Using two extensive case studies, we show how ARNs and statistical theory can be combined to generate and test hypotheses.
KW - Association rules
KW - Association rules network
KW - Data mining
UR - http://www.scopus.com/inward/record.url?scp=62249090283&partnerID=8YFLogxK
U2 - 10.1002/sam.10027
DO - 10.1002/sam.10027
M3 - Article
AN - SCOPUS:62249090283
VL - 1
SP - 260
EP - 279
JO - Statistical Analysis and Data Mining
JF - Statistical Analysis and Data Mining
SN - 1932-1872
IS - 4
ER -