MIME : a framework for interactive visual pattern mining
Faculty of Sciences. Mathematics and Computer Science
New York, N.Y. :ACM, 2011
KDD '11 : proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
University of Antwerp
We present a framework for interactive visual pattern mining. Our system enables the user to browse through the data and patterns easily and intuitively, using a toolbox consisting of interestingness measures, mining algorithms and post-processing algorithms to assist in identifying interesting patterns. By mining interactively, we enable the user to combine their subjective interestingness measure and background knowledge with a wide variety of objective measures to easily and quickly mine the most important and interesting patterns. Basically, we enable the user to become an essential part of the mining algorithm. Our demo currently applies to mining interesting itemsets and association rules, and its extension to episodes and decision trees is ongoing.