@techreport{TD:101447,
	att_abstract={{In this paper, we solve the following data summarization problem: given a multi-dimensional data set augmented with a binary attribute, how can we construct an interpretable and informative summary of the factors affecting the binary attribute in terms of the combinations of values of the dimension attributes?  We refer to such summaries are explanation tables.  We show the hardness of constructing optimally-informative explanation tables from data, and we propose effective and efficient heuristics.  The proposed heuristics are based on sampling and include optimizations related to computing the information content of a summary from a sample of the data.  Using real data sets, we demonstrate the advantages of explanation tables compared to related approaches that can be adapted to solve our problem, and we show significant performance benefits of our optimizations.}},
	att_authors={ds8961},
	att_categories={C_NSS.2, C_BB.1, C_IIS.2},
	att_copyright={{VLDB Foundation}},
	att_copyright_notice={{The definitive version was published in Very Large Databases, 2014. {{, Volume 8}}{{, Issue 1}}{{, 2014-09-30}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101447},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101447_DS1_2014-07-30T02:25:09.530Z.pdf},
	author={Divesh Srivastava and Kareem El Gebaly and Parag Agrawal, Twitter and Lukasz Golab and Flip Korn, Google},
	institution={{Proceedings of the VLDB Endowment}},
	month={September},
	title={{Interpretable and informative explanations of outcomes}},
	year=2014,
}