2004
DOI: 10.1109/tkde.2004.28
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Mining constrained gradients in large databases

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. [IKA02], which focuses on the notable changes in measures in MDS by comparing a cell (which we refer as probe cell) with its gradient cells, namely… Show more

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Cited by 59 publications
(36 citation statements)
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“…To do so, we compare it to four methods, three of which come from the machine learning literature. Our first baseline, Exact, is similar to Claude, but we removed the approximation scheme presented in 4 -instead we compute the exact the mutual information, as in Equation 6 The method should be slower, but more accurate.…”
Section: View Selectionmentioning
confidence: 99%
“…To do so, we compare it to four methods, three of which come from the machine learning literature. Our first baseline, Exact, is similar to Claude, but we removed the approximation scheme presented in 4 -instead we compute the exact the mutual information, as in Equation 6 The method should be slower, but more accurate.…”
Section: View Selectionmentioning
confidence: 99%
“…Experimental comparison, using data from a real collaborative tagging system (Last.fm), against both recent tag-aware and traditional (non tag aware) item recommendation algorithms indicates significant improvements in recommendation quality. [3] Mining Constrained Gradients in Large Databases: liveset-Driven algorithm finds all good gradient-probe cell pairs in one search pass. It utilizes measure-value analysis and dimension-match analysis in a set-oriented manner, to achieve bidirectional pruning between the sets of hopeful probe cells and of hopeful gradient cells.…”
Section: Literature Surveymentioning
confidence: 99%
“…This includes (but is not limited to) outlier detection in multidimensional data [11,19,21], cubegrade generation [10], constrained gradient analysis [6], cube mining [22,13], cube modeling and compression [2,3,12,19], and query answer approximation [2,4,16].…”
Section: Related Workmentioning
confidence: 99%