Title :
Minability through Compression
Author_Institution :
Dept. of Comput. Sci., Univ. of Massachusetts Boston, Boston, MA, USA
Abstract :
We offer an experimental proof that the application of compression to data files can be used as a evaluation technique for minability of the data. This is based on the fact that the presence of patterns embedded in data has an influence of compressibility.
Keywords :
data compression; data mining; data compressibility; data file compression; data minability evaluation technique; Association rules; Compression algorithms; Correlation; Entropy; Probability distribution; Random variables; Kronecker product; LZW; data mining; lossless compression; market basket data; patterns;
Conference_Titel :
Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2013 15th International Symposium on
Conference_Location :
Timisoara
Print_ISBN :
978-1-4799-3035-7
DOI :
10.1109/SYNASC.2013.11
