Seno, M. & Karypis, G. (2002), SLPMiner: An algorithm for finding frequent sequential patterns using length decreasing support constraint, in `Proceedings of the 2nd IEEE International Conference on Data Mining (ICDM)', IEEE, Maebashi City, Japan, pp. 418--425.  Home/Search Document Details and Download
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Discovering Frequent Geometric Subgraphs - Kuramochi, Karypis (2002) (2 citations) Self-citation (Karypis) (Correct)
.... between the time complexity and the size of the transaction is due to the fact that the algorithm needs to explore a much higher search space, and is consistent with the time increases for other pattern discovery algorithms, such as those for finding frequent itemsets [12] and sequential patterns [13]. Nevertheless, gFSG is able to mine the largest dataset with a support of 0.25 in less than two hours. Also, comparing the scale invariant with the scale variant experiments, we can see that as before, finding the scale variant patterns is faster by about a factor of two. scaling. t[sec] 1 ....
M. Seno and G. Karypis. SLPMiner: An algorithm for finding frequent sequential patterns using length decreasing support constraint. Technical Report 02-023, Department of Computer Science, University of Minnesota, 2002.
Discovering Frequent Geometric Subgraphs - Kuramochi, Karypis (2002) (2 citations) Self-citation (Karypis) (Correct)
.... between the time complexity and the size of the transaction is due to the fact that the algorithm needs to explore a much higher search space, and is consistent with the time increases for other pattern discovery algorithms, such as those for finding frequent itemsets [12] and sequential patterns [13]. Nevertheless, gFSG is able to mine the largest dataset with a support of 0.25 in less than two hours. Also, comparing the scale invariant with the scale variant experiments, we can see that as before, finding the scale variant patterns is faster by about a factor of two. Table 5: Running times ....
M. Seno and G. Karypis. SLPMiner: An algorithm for finding frequent sequential patterns using length decreasing support constraint. Technical Report 02-023, Department of Computer Science, University of Minnesota, 2002.
A Delivery Framework for Health Data Mining and Analytics - McAullay, Williams.. (Correct)
No context found.
Seno, M. & Karypis, G. (2002), SLPMiner: An algorithm for finding frequent sequential patterns using length decreasing support constraint, in `Proceedings of the 2nd IEEE International Conference on Data Mining (ICDM)', IEEE, Maebashi City, Japan, pp. 418--425.
BIDE: Efficient Mining of Frequent Closed Sequences - Wang, Han (Correct)
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M. Seno, G. Karypis, SLPMiner: An algorithm for finding frequent sequential patterns using lengthdecreasing support constraint. In ICDM'02,, Maebashi, Japan, Dec. 2002.
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