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This paper presents the Partial-Value Association Discovery (PVAD) algorithm which discovers variable relations/associations that exist in partial ranges of variable values from large amounts of data in a computationally efficient way.
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Cases with the highest 100 efficiencies are analyzed to develop an understanding of ranges of variables that are favorable.
Cases with highest 100 efficiencies are analyzed to develop an understanding of ranges of variables that are favorable.
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