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--- preferencebasedpatternminingtutorial [2016/09/07 16:53]
mplantev [Tutorial Description]
+++ preferencebasedpatternminingtutorial [2016/09/07 16:59]
mplantev [Outline]
@@ Line 33: / Line 33: @@
 the pattern mining task to an optimization problem.
+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 pattern mining relies on the automatic acquisition of these
+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, this mining process raises new challenges: what
+user feedback to capture? How to elicit a preference model? How to instantly
+mine patterns based on preferences?
+==== Relevance ====
+Preferences are a way to put the user in the loop of the data mining process.
+More generally, user-centered methods are crucial in the field of exploratory
+data analysis (Information Retrieval, OnLine Analytical Processing, Knowledge
+Discovery in Databases). They are based primarily on subjective knowledge of
+the user which results in the form of preferences. Last years, part of the work in
+pattern mining follows that direction. It seems important to present a tutorial
+on the motivations, challenges and methods at the intersection of preferences
+and pattern mining.
+==== Target Audience ====
+The target audience of this tutorial is formed by researchers and practitioners
+in both academia and industry interested in getting a high-level, comprehensive
+overview of how high-quality patterns can be mined and employed by taking
+into account the end-user’s preferences. Knowledge on constraint-based pattern
+mining, preferences and constraints are not required, we will provide a quick
+overview of these topics.
-==== Context and Goal ====
-==== Context and Goal ====
 =====  Outline =====
+<note warning>This is neither  a tutorial on constraint-based pattern mining nor on preference learning. </note>

Marc Plantevit

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