Call for Papers: ECMLPKDD Workshop on Mining and exploiting interpretable local patterns

September 28, 2012, Bristol/UK
Submission deadline: June 29, 2012

Workshop homepage:

Topics of interest:

* Actionable patterns
* Applications of local pattern mining, e.g. in clinico-genomic,
fraud detection or marketing settings
* Interactive data-mining
* Interpretable models
* Measures and optimization of interestingness for rules and models
* Pattern ordering and pattern set selection
* Scalability of local pattern mining
* Subgroup discovery

Submission details:

The papers must be written in English and formatted according to the Springer LNCS guidelines. Authors instructions and style files can be downloaded at: The maximum length of the papers is 16 pages.

The complete submission process will be managed via Easy Chair:

Detailed description:

Local patterns, like itemsets, correlations, contrast sets or subgroups, are valuable nuggets for a variaty 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 successfull 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 interestinness 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? Beside papers focusing on such questions, we welcome case studies of successful descriptive pattern mining applications. Papers combining applications and theoretical contributions are particularly welcome.