Improve Frequent Closed Itemsets Mining over Data Stream with Bitmap

Frequent itemsets mining is an important problem in data mining. Frequent closed itemsets mining provides complete and condensed information for frequent pattern analysis thus reduces the memory cost without accuracy loss. More research focus on stream mining with the more application of stream. Stream is fast and unlimited thus data had to be stored in limited memory, how to save running time and memory usage is the most important target. In this paper, we propose an improved frequent closed itemsets mining method based on traditional stream mining algorithm CFI-stream with bitmap coding named CLIMB (closed itemset mining with bitmap) over stream´s sliding window. The distinct items are maintained in memory in lexicographic order and each itemset is coded to bit-sequence with the order of items, moreover, the bit-sequence is split into sections to be recoded to reduce the memory cost. The experimental results on real-life show that CLIMB algorithm is effective and efficient.