Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams
Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams
复制标题
使用零抑制二元决策图快速挖掘高维表达对比模式
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
J. Bailey
中科院分区：
文献类型：
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作者：
Elsa Loekito;J. Bailey
对比模式是比较多维数据集的一种非常重要的方式。这种模式能够捕捉两类数据之间差异较大的区域，对于人类专家和分类器的构建是有用的。然而，当维度数量很大时，挖掘这样的模式尤其具有挑战性。本文描述了一种基于零抑制二叉决策图(ZBDDs)的挖掘多种对比模式的新技术。ZBDDS是一种处理稀疏数据的强大数据结构。我们研究了简单对比模式(如新兴模式)的挖掘，以及更新颖、更复杂的对比模式的挖掘，我们称之为析取新兴模式。一项性能研究表明，我们的ZBDD技术具有高度的可扩展性，大大改进了对新兴模式的挖掘技术，并且可以有效地从具有数千个属性的数据集中发现复杂的对比。
Patterns of contrast are a very important way of comparing multi-dimensional datasets. Such patterns are able to capture regions of high difference between two classes of data, and are useful for human experts and the construction of classifiers. However, mining such patterns is particularly challenging when the number of dimensions is large. This paper describes a new technique for mining several varieties of contrast pattern, based on the use of Zero-Suppressed Binary Decision Diagrams (ZBDDs), a powerful data structure for manipulating sparse data. We study the mining of both simple contrast patterns, such as emerging patterns, and more novel and complex contrasts, which we call disjunctive emerging patterns. A performance study demonstrates our ZBDD technique is highly scalable, substantially improves on state of the art mining for emerging patterns and can be effective for discovering complex contrasts from datasets with thousands of attributes.