TY - JOUR
T1 - Data Mining Using Two-Dimensional Optimized Association Rules
T2 - Scheme, Algorithms, and Visualization
AU - Fukuda, Takeshi
AU - Morimoto, Yasuhiko
AU - Tokuyama, Takeshi
AU - Morishita, Shinichi
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 1996/6
Y1 - 1996/6
N2 - 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 in a planar region P tend to use card loan 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, and constructed a system for visualizing the rules.
AB - 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 in a planar region P tend to use card loan 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, and constructed a system for visualizing the rules.
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U2 - 10.1145/235968.233313
DO - 10.1145/235968.233313
M3 - Article
AN - SCOPUS:0030156999
VL - 25
SP - 13
EP - 23
JO - SIGMOD Record
JF - SIGMOD Record
SN - 0163-5808
IS - 2
ER -