A. Nanopoulos, D. Katsaros, Y. Manolopoulos, A data mining algorithm for generalized web prefetching, IEEE Trans. Knowl. Data Eng. 15 (5) (2003) 1155--1169. Home/Search Document Details and Download
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A. Nanopoulos, D. Katsaros, Y. Manolopoulos, A data mining algorithm for generalized web prefetching, IEEE Trans. Knowl. Data Eng. 15 (5) (2003) 1155--1169.
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A. Nanopoulos, D. Katsaros, and Y. Manolopoulos. A data mining algorithm for generalized Web prefetching. IEEE Transactions on Knowledge and Data Engineering, 15(5):1155--1169, 2003.
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A. Nanopoulos, D. Katsaros, and Y. Manolopoulos. A data mining algorithm for generalized Web prefetching. IEEE Transactions on Knowledge and Data Engineering, 2002. to appear.
Efficient Storage and Querying of Sequential.. - Nanopoulos.. (2003) Self-citation (Nanopoulos Manolopoulos) (Correct)
....in Ref. 2] Following the approach of Refs. 38,22] so as to examine the worst case for equivalent sets, according to Lemma 4 we consider sequential patterns with elements being single items (singletons) Our implementation is based on a modified version of the generator developed in Ref. [25], which was used to produce sequential patterns for the case of web user traversals (see also Ref. 22] The reason is because they actually consist of sequences of single items. The generator builds a pool of sequences, each of them being a sequence of pairwise distinct items from a domain I. ....
....is a random variable that follows Poisson distribution with a given mean value. A new pool sequence keeps a number of items from the previous one, determined by the correlation factor. Since we are interested in the effect of item ordering within sequences, we modified the generator of Ref. [25] so as to perform a random permutation of the common items before inserting them in the new pool sequence. This results into sequences that contain items with different ordering, thus examines the impact of this factor. The rest of each sequence is formed by selecting items from I with uniform ....
A. Nanopoulos, D. Katsaros, Y. Manolopoulos, A data mining algorithm for generalized web prefetching, IEEE Transactions on Knowledge and Data Engineering, 2002, in press.
Exploiting Web Log Mining for Web Cache Enhancement - Nanopoulos, Katsaros.. Self-citation (Nanopoulos Katsaros Manolopoulos) (Correct)
....is improved by this pruning criterion. The effectiveness of pruning is verified by experimental results in Section 5. More details can be found in [32, 33] whereas a further examination of the generalization of the described prefetching algorithm compared to existing ones, can be found in [31]. Finally, it worths mentioning that the proposed pruning criterion is used in combination with the support pruning criterion. However, the modified apriori criterion is applied (steps 6 9 of the GenCandidates procedure) which examines only the subpaths of a sequence and not any arbitrary ....
A. Nanopoulos, D. Katsaros, and Y. Manolopoulos. A data mining algorithm for generalized Web prefetching. IEEE Transactions on Knowledge and Data Engi- neering, 2002. to appear.
VOGUE: A Novel Variable Order-Gap State - Machine For Modeling (Correct)
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A. Nanopoulos, D. Katsaros, and Y. Manolopoulos. A data mining algorithm for generalized web prefetching. IEEE Trans. Knowledge and Data Engg., 15(5):1155--1169, 2003.
VOGUE: A Novel Variable Order-Gap State - Machine For Modeling (Correct)
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A. Nanopoulos, D. Katsaros, and Y. Manolopoulos. A data mining algorithm for generalized web prefetching. IEEE TKDE, 15(5):1155--1169, 2003.
Design Keys to Adapt Web Prefetching Algorithms to.. - Domenech.. (Correct)
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A. Nanopoulos, D. Katsaros, and Y. Manolopoulos, "A data mining algorithm for generalized web prefetching." IEEE Trans. Knowl. Data Eng., vol. 15, no. 5, pp. 1155--1169, 2003.
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A. Nanopoulos, D. Katsaros, and Y. Manolopoulos, "A data mining algorithm for generalized web prefetching," IEEE Trans. Knowl. Data Eng., vol. 15, no. 5, 2003.
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Nanopoulos, A., Katsaros, D., Manolopoulos, Y.: A data mining algorithm for generalized web prefetching. IEEE Transactions on Knowledge and Data Engineering, 15 (2003)
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