By Bin Tong, Einoshin Suzuki (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)
This publication constitutes the complaints of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
Read Online or Download Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II PDF
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Extra resources for Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II
Sample text
Pairs with the same key are sent to the same CCReducer. By means of the summary at the previous timestamp and the arriving itemset at the current timestamp, each CCReducer is able to generate candidate sequential patterns of each sequence in the current POI. In line 2 of CCReducer, the multiple output variable is used to output candidate set summary at the current timestamp for the future computation. In lines 6 to 15, CCReducer enumerates each value in the receiving pairs. If the value is a candidate set summary at the previous timestamp, CCReducer puts the candidate into cand set.
1457–1458 (2008) 7. : Semi-supervised Dimensionality Reduction Using Pairwise Equivalence Constraints. In: International Conference on Computer Vision Theory and Applications (VISAPP), pp. 489–496 (2008) 8. : Neighborhood Preserving Based Semi-supervised Dimensionality Reduction. Electronics Letters 44, 1190–1191 (2008) 9. : Enhancing Semi-supervised Clustering: A Feature Projection Perspective. In: Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.
We will derive some preliminaries in Section 2. The proposed algorithm DPSP will be introduced in Section 3. Some experiments to evaluate the performance will be shown in Section 4. Finally, the conclusion is given in Section 5. 2 Related Works After the first work addressing the sequential pattern mining problem in [1], many research works are proposed to solve the static sequential pattern mining problem [2], and the incremental sequential pattern mining problem [10]. As for the progressive sequential pattern mining problem, new data arrive at the database and obsolete data are deleted at the same time.