IPUMS Census Database Data Set
Below are papers that cite this data set, with context shown.
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Ke Wang and Shiyu Zhou and Ada Wai-Chee Fu and Jeffrey Xu Yu. Mining Changes of Classification by Correspondence Tracing. SDM. 2003.
German Credit Data from the UCI Repository of Machine Learning Databases , and IPUMS Census Data from . These data sets were chosen because no special knowledge is required to understand the addressed applications. To verify if the proposed method finds the changes that are supposed to be found, we need to know such
Stephen D. Bay and Michael J. Pazzani. Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov, 5. 2001.
example, if we are mining at a support difference of 10% and group A has a support of 11% we still need to mine group B as long as its support is non-zero. STUCCO was very fast and did well on all data sets, even on Mushroom and IPUMS which are among the most difficult data sets for mining algorithms. STUCCO was slower than Apriori on UCI Admissions by a factor of approximately three. This is probably
Chris Giannella and Bassem Sayrafi. An Information Theoretic Histogram for Single Dimensional Selectivity Estimation. Department of Computer Science, Indiana University Bloomington.
CUP 1998 Data" heading, cup98lrn file -- attributes 199 (IC1, median household income) and 472 (TARGET D, donation amount quantized into 60 groups), respectively. The ipums25, ipums51, and ipums52 datasets were found under the IPUMS Census Data" heading, ipums.la.99 file -- attributes 25 (eldch, age of the eldest child in the household), 51 (incbus represents business income), and 52 (incfarm