Abstract
In this paper, we address a data-mining problem that is the discovery of local sequential patterns from a set of long sequences. Each local sequential pattern is represented by a pattern A→B and a time period in which A→B is frequent. Such patterns are actually very common in practice and are potentially very useful. However it is impractical to use traditional methods on this problem directly. We propose a suffix-tree-like data structure for indexing the instances of the patterns. Based on this index, our mining method can discover all locally frequent patterns after one scan of the sequences. We have analyzed the behavior of the problem and evaluated the performance of our algorithm with both synthetic and real data. The results correspond with the definition of the problem and verify the superiority of our approach.
The research has been supported in part of Chinese national key fundamental research program (no, G1998030414) and Chinese national fund of natural science (no. 79990580)
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Jin, X., Lu, Y., Shi, C. (2002). Discovering Local Patterns from Multiple Temporal Sequences. In: Shafazand, H., Tjoa, A.M. (eds) EurAsia-ICT 2002: Information and Communication Technology. EurAsia-ICT 2002. Lecture Notes in Computer Science, vol 2510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36087-5_4
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DOI: https://doi.org/10.1007/3-540-36087-5_4
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