Abstract
We use closed pattern mining to discover user preferences in appointments in order to build structured solutions for a calendar assistant. Our choice of closed patterns as a user preference representation is based on both theoretical and practical considerations supported by Formal Concept Analysis. We simulated interaction with a calendar application using 16 months of real data from a user’s calendar to evaluate the accuracy and consistency of suggestions, in order to determine the best data mining and solution generation techniques from a range of available methods. The best performing data mining method was then compared with decision tree learning, the best machine learning algorithm in this domain. The results show that our data mining method based on closed patterns converges faster than decision tree learning, whilst generating only consistent solutions. Thus closed pattern mining is a better technique for generating appointment attributes in the calendar domain.
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Krzywicki, A., Wobcke, W. (2008). Closed Pattern Mining for the Discovery of User Preferences in a Calendar Assistant. In: Nguyen, N.T., Katarzyniak, R. (eds) New Challenges in Applied Intelligence Technologies. Studies in Computational Intelligence, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79355-7_7
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DOI: https://doi.org/10.1007/978-3-540-79355-7_7
Publisher Name: Springer, Berlin, Heidelberg
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