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Finding Composite Episodes

机译:寻找复合情节

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Mining frequent patterns is a major topic in data mining research, resulting in many seminal papers and algorithms on item set and episode discovery. The combination of these, called composite episodes, has attracted far less attention in literature, however. The main reason is that the well-known frequent pattern explosion is far worse for composite episodes than it is for item sets or episodes. Yet, there are many applications where composite episodes are required, e.g., in developmental biology were sequences containing gene activity sets over time are analyzed. This paper introduces an effective algorithm for the discovery of a small, descriptive set of composite episodes. It builds on our earlier work employing MDL for finding such sets for item sets and episodes. This combination yields an optimization problem. For the best results the components descriptive power has to be balanced. Again, this problem is solved using MDL.
机译:频繁模式的挖掘是数据挖掘研究中的一个主要主题,因此产生了许多关于项目集和情节发现的开创性论文和算法。但是,这些组合称为复合情节,在文学界吸引的关注已经很少。主要原因是,对于复合情节而言,众所周知的频繁模式爆炸比对于项目集或情节而言要差得多。然而,在许多应用中,需要复合事件,例如,在发育生物学中,分析随时间推移包含基因活性集的序列。本文介绍了一种有效的算法,可用于发现少量描述性的复合情节。它建立在我们早期使用MDL的工作的基础上,该MDL用于查找项目集和情节的此类集。这种结合产生了优化问题。为了获得最佳结果,必须平衡组件的描述能力。同样,使用MDL解决了这个问题。

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