Data mining promises to discover valid and potentially useful patterns in data. Often, discovered patterns are not useful to the user."Actionability" addresses this problem in that a pattern is deemed actionable if the user can act upon it in her favor. We introduce the notion of "action" as a domain-independent way to model the domain knowledge. Given a… (More)
Unexpected rules are interesting because they are either previously unknown or deviate from what prior user knowledge would suggest. In this paper, we study three important issues that have been previously ignored in mining unexpected rules. First, the unexpectedness of a rule depends on <i>how</i> the user prefers to apply the prior knowledge to a given… (More)
Iceberg-cube mining is to compute the GROUP BY partitions , for all GROUP BY dimension lists, that satisfy a given aggregate constraint. Previous works have pushed anti-monotone constraints into iceberg-cube mining. However , many useful constraints are not anti-monotone. In this paper, we propose a novel strategy for pushing general aggregate constraints,… (More)
The iceberg cube mining computes all cells v, corresponding to GROUP BY partitions, that satisfy a given constraint on aggregated behaviors of the tuples in a GROUP BY partition. The number of cells often is so large that the result cannot be realistically searched without pushing the constraint into the search. Previous works have pushed antimonotone and… (More)
Data mining focuses on patterns that summarize the data. In this paper, we focus on mining patterns that could change the state by responding to opportunities of actions.
Outlier analysis is an important task in data mining and has attracted much attention in both research and applications. Previous work on outlier detection involves different types of databases such as spatial databases, time series databases, biomedical databases, etc. However, few of the existing studies have considered spatial networks where points… (More)