Streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding window model for processing and mining data streams. However, for some applications, other stream processing models such as the time-fading model and the landmark model are more appropriate. In this paper, we propose mining algorithms that use (i) the time-fading model and (ii) the landmark model to discover frequent patterns from streams of uncertain data.
