Compression Picks Item Sets That Matter
Compression Picks Item Sets That Matter
复制标题
压缩选择重要的项目集
DOI：
10.1007/11871637_59
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
A. Siebes
中科院分区：
文献类型：
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作者：
M. Leeuwen;Jilles Vreeken;A. Siebes
找到一组全面的模式来真正捕捉数据库的特征是一件复杂的事情。频繁的项集挖掘尝试这样做，但低支持级别通常会导致过多的项集。最近，我们证明了通过使用MDL，我们能够选择少量的项目集来很好地压缩数据[11]。在这里，我们表明，这个小的集合是一个很好的近似的基础数据分布。在基于MDL的分类器中使用小集合导致与众所周知的基于规则归纳和关联规则的方法相当的性能。优点是不需要手动设置参数，并且只使用很少的项目集。分类分数表明，通过压缩选择项目集是挖掘有趣模式的一种优雅方式，随后可以在许多应用程序中找到用途。
Finding a comprehensive set of patterns that truly captures the characteristics of a database is a complicated matter. Frequent item set mining attempts this, but low support levels often result in exorbitant amounts of item sets. Recently we showed that by using MDL we are able to select a small number of item sets that compress the data well [11]. Here we show that this small set is a good approximation of the underlying data distribution. Using the small set in a MDL-based classifier leads to performance on par with well-known rule-induction and association-rule based methods. Advantages are that no parameters need to be set manually and only very few item sets are used. The classification scores indicate that selecting item sets through compression is an elegant way of mining interesting patterns that can subsequently find use in many applications.