Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Prediction, Learning, and Games
Prediction, Learning, and Games
Discovering interesting patterns through user's interactive feedback
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Subgroup discovery for election analysis: a case study in descriptive data mining
DS'10 Proceedings of the 13th international conference on Discovery science
Non-redundant subgroup discovery in large and complex data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
MIME: a framework for interactive visual pattern mining
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Data Mining and Knowledge Discovery
Maximizing Non-monotone Submodular Functions
SIAM Journal on Computing
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Linear space direct pattern sampling using coupling from the past
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Online learning to diversify from implicit feedback
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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It is known that productive pattern discovery from data has to interactively involve the user as directly as possible. State-of-the-art toolboxes require the specification of sophisticated workflows with an explicit selection of a data mining method, all its required parameters, and a corresponding algorithm. This hinders the desired rapid interaction---especially with users that are experts of the data domain rather than data mining experts. In this paper, we present a fundamentally new approach towards user involvement that relies exclusively on the implicit feedback available from the natural analysis behavior of the user, and at the same time allows the user to work with a multitude of pattern classes and discovery algorithms simultaneously without even knowing the details of each algorithm. To achieve this goal, we are relying on a recently proposed co-active learning model and a special feature representation of patterns to arrive at an adaptively tuned user interestingness model. At the same time, we propose an adaptive time-allocation strategy to distribute computation time among a set of underlying mining algorithms. We describe the technical details of our approach, present the user interface for gathering implicit feedback, and provide preliminary evaluation results.