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preferencebasedpatternminingtutorial [2016/09/07 16:47] mplantev [Speakers] |
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+ | ===== Tutorial Description ===== | ||
+ | ==== Context and Goal ==== | ||
- | Email: bruno.cremilleux@unicaen.fr | + | The paradigm of constraint-based pattern mining assumes a strong assumption: the user knows what he/she is looking for and, even more, he/she is able to express queries to pattern mining solvers. In practice, he/she has only a vague idea of what useful patterns could be and it is very hard to derive appropriate queries for the solvers. It explains |
- | Web: https://cremilleux.users.greyc.fr/ | + | mining. |
- | Marc Plantevit, Université de Lyon, France. He received his PhD in computer | + | |
- | science in 2008 from the University of Montpellier. He has been an associate | + | |
- | professor in the computer science department | + | |
- | since 2009. His research interest include constraint-based pattern | + | |
- | Currently, he is very interested with sophisticate pattern domains (dynamic/ | + | |
- | attributed graphs) | + | |
- | mining. | + | |
- | Email: marc.plantevit@liris.cnrs.fr | + | |
- | Web: http:// | + | |
- | Arnaud Soulet, Université François Rabelais de Tours, France. He received | + | ==== Content ==== |
- | his PhD in 2006 from the University | + | |
- | in computer science since 2007 at the University François Rabelais | + | Constraint-based pattern mining is now a mature domain of data mining that makes it possible to handle various different pattern domains (e.g., itemsets, sequences, graphs, dynamic graphs) with a large variety |
- | He has an expertise | + | |
- | mining process | + | |
- | Email: arnaud.soulet@univ-tours.fr | + | Preferences |
- | Web: http://www.info.univ-tours.fr/~soulet/ | + | pattern mining, the utility functions finely measure the interest of a pattern |
- | ===== Tutorial Description ===== | + | and can be seen as a quantitative preference model. Many other mechanisms |
+ | have been developed such as mining | ||
+ | one measure (top-k patterns) or more (skyline patterns), reducing | ||
+ | redundancy by integrating subjective interestingness and then putting | ||
+ | the pattern | ||
+ | |||
+ | |||
+ | However, all of the above approaches assume that preferences are explicit and | ||
+ | given in the process. In practice, the user has only a vague idea of what useful | ||
+ | patterns could be and there is a need to elicit preferences. The recent research | ||
+ | field of interactive | ||
+ | preferences. Basically, its principle is to repeat a short mining loop centered | ||
+ | on the user. At each iteration, only some patterns are mined and the user has to | ||
+ | indicate those that are relevant (by liking/disliking, rating, ranking). The | ||
+ | user feedback improves an automatically learned model of preferences that will | ||
+ | refine the pattern mining step in the next iteration. A great advantage is the user | ||
+ | does not have to explicit her preference model. In addition, each iteration is fast | ||
+ | and it does not overwhelm the user with a huge collection of patterns impossible | ||
+ | to analyze. Interestingly, | ||
+ | user feedback to capture? How to elicit a preference model? How to instantly | ||
+ | mine patterns based on preferences? | ||
+ | ==== Context and Goal ==== | ||
+ | ==== Context and Goal ==== | ||
===== Outline ===== | ===== Outline ===== |