Mining border descriptions of emerging patterns from dataset pairs
Mining border descriptions of emerging patterns from dataset pairs
复制标题
从数据集对中挖掘新兴模式的边界描述
DOI：
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发表时间：
2005
影响因子：
2.7
通讯作者：
Jinyan Li
中科院分区：
文献类型：
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作者：
Guozhu Dong;Jinyan Li
从一对数据集中挖掘变化或差异或其他比较模式是一个有趣的问题。本文主要研究的是比较模式中的新兴模式的挖掘。新兴模式由EP表示，并被定义为支持度从一个数据集到另一个数据集以大比例增加的模式。EP的数量有时是巨大的。为了提供一个良好的结构，并减少挖掘结果的大小，我们使用边界来简洁地描述在无损的方式EP的大集合。这样的边界仅由集合中的最小EP（在集合包含下）和最大EP组成。我们还提出了一个算法，有效地计算一些所需的EP的边界，通过操纵输入边界。我们对UCI Repository中的许多数据集和最近的癌症诊断数据集的经验表明：EP模式类型和我们的算法都有助于构建准确的分类器，并有助于挖掘多因素相互作用，例如，可能导致癌症发展的最小基因组。
The mining of changes or differences or other comparative patterns from a pair of datasets is an interesting problem. This paper is focused on the mining of one type of comparative pattern called emerging patterns. Emerging patterns are denoted by EPs and are defined as patterns for which support increases from one dataset to the other with a big ratio. The number of EPs is sometimes huge. To provide a good structure for and to reduce the size of mining results, we use borders to concisely describe large collections of EPs in a lossless way. Such a border consists of only the minimal (under set inclusion) and the maximal EPs in the collection. We also present an algorithm for efficiently computing the borders of some desired EPs by manipulating the input borders only. Our experience with many datasets in the UCI Repository and recent cancer diagnosis datasets demonstrated that: Both the EP pattern type and our algorithm are useful for building accurate classifiers and useful for mining multifactor interactions, for example, minimal gene groups potentially responsible for the development of cancer.
影响因子：
50.3
作者：
Yeoh, EJ;Ross, ME;Downing, JR
通讯作者：
Downing, JR