Incremental updating algorithm association rules
The update of insertion or deletion only needs one scan of the current window, which improves efficiency.
The capability of queries at arbitrary time on the whole current window is achieved by Query Manager Procedure, which can capture the phenomenon of concept drift of data stream in time.
This paper focuses on mining maximal frequent itemsets approximately over a stream landmark model.
A false negative method is proposed based on Chernoff Bound to save the computing and memory cost.
Abstract: Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams.
Many algorithms came into existence for mining association rules.
Since the databases in the real world are subjected to frequent changes, the algorithms need to be rerun to generate association rules that can reflect record insertions.
It causes overhead the algorithm needs to scan entire database every time and repeat the process.
Incremental updating of mined association rules is challenging.