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...
Data mining aims at discovering important and previously unknown patterns from the dataset in the underlying database. Database mining performs mining directly on data stored in r...
We present an algorithm for mining association rules from relational tables containing numeric and categorical attributes. The approach is to merge adjacent intervals of numeric v...
This article tries to give an answer to a fundamental question in temporal data mining: "Under what conditions a temporal rule extracted from up-to-date temporal data keeps i...
Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns in time se...