Frequent Item Mining Using Damped Window Model

Abstract: In the modern days, streams of data can be constantly generated by sensors in various real-life applications such as environment surveillance. Due to the continous flow of transactions, data in these streams can be uncertain. To discover useful and potential knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. Most of the algorithm 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 are more appropriate. In this paper, we propose mining algorithms that use the damped model to discover frequent patterns from streams of uncertain data.

Keywords: Knowledge discovery, data mining techniques, data streams, frequent item sets, probabilistic data