Incremental Mining of Sequential Patterns over a Stream Sliding Window

Authors:
Chin-Chuan Ho;Hua-Fu Li;Fang-Fei Kuo;Suh-Yin Lee
Affiliations:
National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

Incremental mining of sequential patterns from data streams is one of the most challenging problems in mining data streams. However, previous work of mining sequential patterns from data streams is almost focused on mining of patterns from stream of item-sequences, not stream of itemset-sequences. In this paper, we propose an efficient single-pass algorithm, called IncSPAM, to maintain the set of sequential patterns from itemset-sequence streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed IncSPAM algorithm is efficient for mining sequential patterns over data streams.