SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
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Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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ICDE '01 Proceedings of the 17th International Conference on Data Engineering
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
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ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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Sequential pattern mining is an important problem for data mining with broad applications. This paper presents a first-Horizontal-last-Vertical scanning database Sequential pattern Mining algorithm (HVSM). HVSM considers a database as a vertical bitmap. The algorithm first extends itemsets horizontally, and digs out all one-large-sequence itemsets. It then extends the sequence vertically and generates candidate large sequence. The candidate large sequence is generated by taking brother-nodes as child-nodes. The algorithm counts the support by recording the first TID mark (1st-TID). Experiments show that HVSM algorithm can find frequent sequences faster than SPAM algorithm in mining the large transaction databases.