%0 Conference Paper %1 lemmerich2012generic %A Lemmerich, Florian %A Becker, Martin %A Atzmueller, Martin %B European Conference on Machine Learning and Knowledge Discovery in Databases %C Berlin, Heidelberg %D 2012 %E Flach, Peter A. %E De Bie, Tijl %E Cristianini, Nello %I Springer Berlin Heidelberg %K 2012 cv diss diss:allmypubs emm everyaware exceptional fptree gptree inthesis mining model myown p21 project:bmbf selected subgroup %P 277--292 %R 10.1007/978-3-642-33486-3_18 %T Generic Pattern Trees for Exhaustive Exceptional Model Mining %U https://doi.org/10.1007/978-3-642-33486-3_18 %X Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search. %@ 978-3-642-33486-3

@inproceedings{lemmerich2012generic, abstract = {Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.}, added-at = {2012-08-29T16:01:09.000+0200}, address = {Berlin, Heidelberg}, author = {Lemmerich, Florian and Becker, Martin and Atzmueller, Martin}, biburl = {https://www.bibsonomy.org/bibtex/28de4ecaed51fdd8466ec8b3205a58c6d/becker}, booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases}, doi = {10.1007/978-3-642-33486-3_18}, editor = {Flach, Peter A. and De Bie, Tijl and Cristianini, Nello}, interhash = {f4e5882d8095a60e7164b3dc65c727b2}, intrahash = {8de4ecaed51fdd8466ec8b3205a58c6d}, isbn = {978-3-642-33486-3}, keywords = {2012 cv diss diss:allmypubs emm everyaware exceptional fptree gptree inthesis mining model myown p21 project:bmbf selected subgroup}, pages = {277--292}, publisher = {Springer Berlin Heidelberg}, timestamp = {2021-07-25T23:55:54.000+0200}, title = {Generic Pattern Trees for Exhaustive Exceptional Model Mining}, url = {https://doi.org/10.1007/978-3-642-33486-3_18}, year = 2012 }