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Guozhu Dong, Jiawei Han, Joyce M.W. Lam, Jian Pei, Ke Wang, Wei Zou, "Mining Constrained Gradients in Large Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 8, pp. 922938, August, 2004.  
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@article{ 10.1109/TKDE.2004.28, author = {Guozhu Dong and Jiawei Han and Joyce M.W. Lam and Jian Pei and Ke Wang and Wei Zou}, title = {Mining Constrained Gradients in Large Databases}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {16}, number = {8}, issn = {10414347}, year = {2004}, pages = {922938}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.28}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Mining Constrained Gradients in Large Databases IS  8 SN  10414347 SP922 EP938 EPD  922938 A1  Guozhu Dong, A1  Jiawei Han, A1  Joyce M.W. Lam, A1  Jian Pei, A1  Ke Wang, A1  Wei Zou, PY  2004 KW  Data cube KW  data mining KW  gradient analysis KW  iceberg query KW  antimonotonicity KW  dimensionbased pruning KW  constraintbased pruning KW  complex measures. VL  16 JA  IEEE Transactions on Knowledge and Data Engineering ER   
Abstract—Many data analysis tasks can be viewed as search or mining in a multidimensional space (MDS). In such MDSs, dimensions capture potentially important factors for given applications, and cells represent combinations of values for the factors. To systematically analyze data in MDS, an interesting notion, called "cubegrade” was recently introduced by Imielinski et al. [CHECK END OF SENTENCE], which focuses on the notable changes in measures in MDS by comparing a cell (which we refer to as
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