Nanopoulos, A., Katsaros, D., Manolopoulos, Y.: Effective Prediction of Web-user Accesses: A Data Mining Approach: Proceedings of the Workshop WEBKDD, San Francisco, CA, (2001)  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... Some examples of these services include redesigning of web sites [24] personalization for e commerce sites [27] recommendation of pages [18] construction of web pages in real time [23] adaptation of web pages for wireless devices [4] improvement of web search engines, and prefetching [ 11 ] [ 19] [30] 31 ] The main techniques traditionally used for modeling user s patterns are clustering and association rules. These two approaches produce systems which lack two important characteristics of Web user access: sequentiality and temporality. In this context sequentiality implies reflecting ....

....order in which those web pages are visited. The model expresses those patterns using rules. This ability of detecting patterns constructed with non consecutive sequences introduces the possibility of measuring the distance between the antecedent and the consequent of a rule. Some algorithms, like [19], are designed to detect non consecutive sequences, but there is no indication of the distance between them. In our model the distance between the antecedent and the consequent is measured in terms of the number of user clicks to go from one to the other. To date no model deals with the concept of ....

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Nanopoulos, A., Katsaros, D., Manolopoulos, Y., Effective Prediction of Web-user Accesses: A Data Mining Approach, Proceeding of the WEBKDD 2001.

....the incremental mining versus recomputation. Figure 13 shows that even with an incremental update size of up to one quarter of the size of the original database size, the FS Miner s incremental feature provides significant time savings over full recomputation. 18 7 Related Work Nanpoulos et al. [6] proposed a method for discovering access patterns from web logs based on a new type of association patterns. They handle the order between page accesses, and allow gaps in sequences. They use a candidate generation algorithm that requires multiple scans of the database. Their pruning strategy ....

A. Nanopoulos, D. Katsaros, and Y. Manolopoulos. Effective prediction of web-user accesses: A data mining approach. In WEBKDD Workshop, San Francisco, CA, Aug. 2001.

....for sequence mining [2] because we are concentrating on adjacent tags, disallowing the occurrence of arbitrary tags in between. Conventional sequence mining do not satisfy this requirement. However, some Web usage miners have been designed to distinguish between adjacent and non adjacent events [3, 9, 24, 20]. GroupSupport In most of the above statistics, we juxtapose the frequence of appearance of a tag with the frequency of a group of tags, be it a set or a sequence. We use the term Property Radius Computation method Accuracy model DIAsDEM Workbench TagSupport tag simple statistics ....

A. Nanopoulos, D. Katsaros, and Y. Manolopoulos. Effective prediction of web-user accesses: A data mining approach. In Proceeding of the Workshop WEBKDD

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Nanopoulos, A., Katsaros, D., Manolopoulos, Y.: Effective Prediction of Web-user Accesses: A Data Mining Approach: Proceedings of the Workshop WEBKDD, San Francisco, CA, (2001)

No context found.

A. Nanopoulos, D. Katsaros, and Y. Manolopoulos, "Effective prediction of Web-user accesses: A data mining approach," in Proc. of the Workshop WEBKDD, San Francisco, CA, 2001.

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