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preferencebasedpatternminingtutorial [2016/09/07 16:53] mplantev [Tutorial Description] |
preferencebasedpatternminingtutorial [2016/09/16 13:30] mplantev [Material] |
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* **[[http:// | * **[[http:// | ||
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+ | ===== Material ===== | ||
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+ | Click on the image to see the slides of the tutorial. | ||
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+ | [[http:// | ||
===== Tutorial Description ===== | ===== Tutorial Description ===== | ||
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the pattern mining task to an optimization problem. | 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/ | ||
+ | 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? | ||
+ | |||
+ | ==== 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, | ||
+ | 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 ===== | ===== Outline ===== | ||
+ | |||
+ | |||
+ | <note warning> |