Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. 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. Our experimental results on a real world dataset show that our algorithm is effective and efficient.
A variable size sliding window based frequent itemsets mining algorithm in data stream
