Computing Optimized Rectilinear Regions for Association Rules
Kunikazu Yoda, Takeshi Fukuda, et al.
KDD 1997
We discuss data mining based on association rules for two numeric attributes and one Boolean attribute. For example, in a database of bank customers, "Age" and "Balance" are two numeric attributes, and "CardLoan" is a Boolean attribute. Taking the pair (Age, Balance) as a point in two-dimensional space, we consider an association rule of the form ((Age, Balance) ∈ P) ⇒ (CardLoan = Yes), which implies that bank customers whose ages and balances fall within a planar region P tend to take out credit card loans with a high probability. We consider two classes of regions, rectangles and admissible (i.e., connected and x-monotone) regions. For each class, we propose efficient algorithms for computing the regions that give optimal association rules for gain, support, and confidence, respectively. We have implemented the algorithms for admissible regions as well as several advanced functions based on them in our data mining system named SONAR (System for Optimized Numeric Association Rules), where the rules are visualized by using a graphic user interface to make it easy for users to gain an intuitive understanding of rules.
Kunikazu Yoda, Takeshi Fukuda, et al.
KDD 1997
Yasuhiko Morimoto, Takeshi Fukuda, et al.
IEEE Transactions on Knowledge and Data Engineering
Hisashi Kashima, Tadashi Tsumura, et al.
ICDE 2005
Takeshi Fukuda, Yasuhiko Morimoto, et al.
SIGMOD Record (ACM Special Interest Group on Management of Data)