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| ASCII Text | x | ||
| Doru Tanasa, Brigitte Trousse, "Advanced Data Preprocessing for Intersites Web Usage Mining," IEEE Intelligent Systems, vol. 19, no. 2, pp. 59-65, March/April, 2004. | |||
| BibTex | x | ||
| @article{ 10.1109/MIS.2004.1274912, author = {Doru Tanasa and Brigitte Trousse}, title = {Advanced Data Preprocessing for Intersites Web Usage Mining}, journal ={IEEE Intelligent Systems}, volume = {19}, number = {2}, issn = {1541-1672}, year = {2004}, pages = {59-65}, doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2004.1274912}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - IEEE Intelligent Systems TI - Advanced Data Preprocessing for Intersites Web Usage Mining IS - 2 SN - 1541-1672 SP59 EP65 EPD - 59-65 A1 - Doru Tanasa, A1 - Brigitte Trousse, PY - 2004 KW - data mining KW - Web mining KW - traffic analysis KW - Web site management KW - Web usage mining KW - KDD KW - data preparation KW - user session VL - 19 JA - IEEE Intelligent Systems ER - | |||
In recent years, Web usage mining has emerged as a new field of data mining and gained increasing attention from both the business and research communities. A particular area of importance is data preprocessing for Intersites WUM. The proposed methodology for this process has two main objectives. The first is to use classical preprocessing (data fusion, data cleaning, and data structuration) to significantly reduce, but in a relevant manner, the size of the Web servers? log files. The second is to use advanced data preprocessing, which employs an extra step called data summarization to increase the quality of data obtained after classical preprocessing. To validate this methodology?s efficiency, an experiment joined and analyzed log files from four related servers.