In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
The problem of discovering episode rules from static databases has been studied for years due to its wide applications in prediction. In this paper, we make the first attempt to st...
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...
Abstract. We describe and evaluate the Running Time Advisor (RTA), a system that can predict the running time of a compute-bound task on a typical shared, unreserved commodity host...
We investigate the impact of time on the predictability of sentiment classification research for models created from web logs. We show that sentiment classifiers are time dependen...