Web usage mining: extracting unexpected periods from web logs Export

@article{citeulike:2487337, abstract = {Abstract\ \ Existing Web usage mining techniques are currently based on an arbitrary division of the data (e.g. ” one log per month”) or guided by presumed results (e.g. ” what is the customers' behaviour for the period of Christmas purchases?”). These approaches have two main drawbacks. First, they depend on the above-mentioned arbitrary organization of data. Second, they cannot automatically extract ” seasonal peaks” from among the stored data. In this paper, we propose a specific data mining process (in particular, to extract frequent behaviour patterns) in order to reveal the densest periods automatically. From the whole set of possible combinations, our method extracts the frequent sequential patterns related to the extracted periods. A period is considered to be dense if it contains at least one frequent sequential pattern for the set of users connected to the website in that period. Our experiments show that the extracted periods are relevant and our approach is able to extract both frequent sequential patterns and the associated dense periods.}, author = {Masseglia, F. and Poncelet, P. and Teisseire, M. and Marascu, A.}, citeulike-article-id = {2487337}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10618-007-0080-z}, day = {28}, doi = {10.1007/s10618-007-0080-z}, journal = {Data Mining and Knowledge Discovery}, keywords = {periods, sequential\_patterns, users\_behaviour, web\_usage, web\_users}, month = {February}, number = {1}, pages = {39--65}, posted-at = {2008-05-30 21:16:00}, priority = {1}, title = {Web usage mining: extracting unexpected periods from web logs}, url = {http://dx.doi.org/10.1007/s10618-007-0080-z}, volume = {16}, year = {2008} }

TY - JOUR ID - citeulike:2487337 L3 - citeulike-article-id:2487337 N2 - Abstract  Existing Web usage mining techniques are currently based on an arbitrary division of the data (e.g. “one log per month”) or guided by presumed results (e.g. “what is the customers’ behaviour for the period of Christmas purchases?”). These approaches have two main drawbacks. First, they depend on the above-mentioned arbitrary organization of data. Second, they cannot automatically extract “seasonal peaks” from among the stored data. In this paper, we propose a specific data mining process (in particular, to extract frequent behaviour patterns) in order to reveal the densest periods automatically. From the whole set of possible combinations, our method extracts the frequent sequential patterns related to the extracted periods. A period is considered to be dense if it contains at least one frequent sequential pattern for the set of users connected to the website in that period. Our experiments show that the extracted periods are relevant and our approach is able to extract both frequent sequential patterns and the associated dense periods. IS - 1 JF - Data Mining and Knowledge Discovery EP - 65 TI - Web usage mining: extracting unexpected periods from web logs VL - 16 SP - 39 KW - periods KW - sequential_patterns KW - users_behaviour KW - web_usage KW - web_users AU - Masseglia, F AU - Poncelet, P AU - Teisseire, M AU - Marascu, A PY - 2008/02/28/ UR - http://dx.doi.org/10.1007/s10618-007-0080-z ER -