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\workshopHeader{Mining and Exploiting Interpretable Local Patterns}
{Henrik Grosskreutz, Nikos Karacapilidis and Stefan R\"{u}ping} % organisers
{10:30--16:00} % please use this time format everywhere
{Room: Wills, 3.33}
{Friday}

% Add an introduction to the workshop here
\workshopIntro{Local patterns, like itemsets, correlations, contrast sets or
 subgroups, are valuable nuggets for a variety of applications. Among others,
 they can been used for classification, regression or outlier detection
 tasks. One particular characteristic which makes them stand out from other
 machine learning tools, however, is that (most) local patterns can directly
 be read and interpreted by end users lacking a profound machine-learning
 background.

 This descriptive nature of local patterns makes them useful as a
 source of information for decision making. For example, in the analysis of
 clinical data, understandable models can help the clinician in understanding
 his data and thus making an informed decision about patient treatment. In
 addition, understandable knowledge can help domain experts to discuss the
 analysis results and collaboratively find a good, interesting solution in
 data-intensive settings to help guide the learner when complex background
 knowledge prevents the system from finding a good model without further
 input.

In this workshop, we wish to investigate typical use cases and key
requirements for the successful usage of local pattern mining in applications
where next to the statistical performance of models, the understandability and
interestingness of the models is the key success factor. Here, we are
particularly interested in settings where the data to be mined is large and
complex, preventing investigations of the data without (semi-)automatic
analysis tools.

Key questions to be investigated in the workshop are: which
pattern language is adequate both for the representation of local phenomena
and for the interpretation by the user; How can the raw set of local patterns
be reduced to a representative and manageable subset? What conditions must be
satisfied for a pattern to be actionable? How can feedback about the
understandability and interestingness of partial models be given back to the
system and how can the search be controlled? What is the best way to deal with
the (typically exponential) size of the pattern space? How to design scalable
algorithms?


}


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\timeTitlePeople{10:30--10:45}{Welcome and Introduction}{Stefan R\"uping and
 Nikos Karacapilidis}

\timeTitlePeople{10:45--11:30}{Invited Talk}{Toon Calders}

\timeTitlePeople{11:30--11:50}{Sequential Pattern Analysis in a Student
 Database}{R. Campagni, Donatella Merlini and Renzo Sprugnoli}

\timeTitlePeople{11:50--12:10}{Density and Non-Grid based Subspace Clustering
 via Kernel Density Estimation}{Jing Zhang, Lantao Yu, Hanqi Zhu and Gang Sun}

\timeTitlePeople{12:10--12:30}{Contextual Models for User Interaction on the
 Web}{Peter Haider, Luca Chliarandini, Ulf Brefeld and Alejandro Jaimes}

\timeTitlePeople{12:30--13:15}{Invited Talk }{Igor Trajkovski}

\timeTitle{13:15--14:45}{  Lunch (not provided)}

\timeTitlePeople{14:45--15:30}{Challenge: Results and Systems
 Description}{Stefan R\"uping}

\timeTitlePeople{15:30--15:50}{The Dicode Project}{Nikos Karacapilidis}

\timeTitlePeople{15:50--16:00}{Wrap-up}{All}


\vspace{\interEventSpace}
This workshop is organized in the context of the EU Collaborative
Project ``DICODE - Mastering Data-Intensive Collaboration and
Decision'' which is co-funded by the European Commission under the
contract FP7-ICT-257184.

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