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摘要
En 中文
We propose a temporal dependency, called trend dependency (TD), which captures a significant family of data evolution regularities. An example of such regularity is Salaries of employees generally do not decrease. TDs compare attributes over time using operators of {<, =, >, less than or equal to, greater than or equal to, not equal}. We define a satisfiability problem that is the dual of the logical implication problem for TDs and we investigate the computational complexity of both problems. As TDs allow expressing meaningful trends, mining them from existing databases is interesting. For the purpose of TD mining, TD satisfaction is characterized by support and confidence measures. We study the problem TDMINE: given a temporal database, mine the TDs that conform to a given template and whose support and confidence exceed certain threshold values. The complexity of TDMINE is studied, as well as algorithms to solve the problem.
Keyword:
temporal database
knowledge discovery
data mining
functional dependency
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期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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