We present a method for finding biologically meaningful patterns on metabolic pathways using the SUBDUE graph-based relational learning system. A huge amount of biological data t...
Time series data poses a significant variation to the traditional segmentation techniques of data mining because the observation is derived from multiple instances of the same und...
Burst detection is the activity of finding abnormal aggregates in data streams. Such aggregates are based on sliding windows over data streams. In some applications, we want to mo...
In this paper we present the results of applying data mining techniques to identify patterns and anomalies in air traffic control operational errors (OEs). Reducing the OE rate is ...
The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a g...