浓缩数据立方中约束立方梯度的挖掘(英文)

Mining Constrained Cube Gradient for the Condensed Data Cube

约束立方梯度挖掘是一项重要的挖掘任务,其主要目的是从数据立方中挖掘出满足梯度约束的梯度-探测元组对.然而,现有的研究都是基于一般数据立方的.研究了浓缩数据立方中约束数据立方梯度的挖掘问题.通过扩展LiveSet驱动算法,提出了一个eLiveSet算法.测试表明,该算法在立方梯度挖掘效率上比现有算法要高.

Constrained cube gradient mining is an important mining task and its goal is to extract the pairs of gradient-probe cell that satisfy the gradient constraint from a data cube. However, previous work are explored for a general data cube. In this paper, the problem of the mining constrained cube gradient for a condensed cube is studied. An algorithm named as eLiveSet for the problem is developed through the extension of the existing efficient mining algorithm LiveSet-driven. The experimental results show that the algorithm is more effective than the existing algorithm on the performance of mining constrained cube gradient.