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Dataless Transitions Between Concise Representations of Frequent Patterns

机译：频繁模式的简洁表示之间的无数据转换

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摘要

For many data mining problems in order to solve them it is required to discover frequent patterns. Frequent itemsets are useful e.g. in the discovery of association and episode rules, sequential patterns and clusters. Nevertheless, the number of frequent itemsets is usually huge. Therefore, a number of lossless representations of frequent itemsets have recently been proposed. Two of such representations, namely the closed itemsets and the generators representation, are of particular interest as they can efficiently be applied for the discovery of most interesting non-redundant association and episode rules. On the other hand, it has been proved experimentally that other representations of frequent patterns happen to be more concise and more quickly extractable than these two representations even by several orders of magnitude. Hence, such concise representations seem to be an interesting alternative for materializing and reusing the knowledge of frequent patterns. The problem however arises, how to transform the intermediate representations into the desired ones efficiently and preferably without accessing the database. This article tackles this problem. As a result of investigating the properties of representations of frequent patterns, we offer a set of efficient algorithms for dataless transitioning between them.
机译：为了解决许多数据挖掘问题，需要发现频繁的模式。频繁的项目集很有用，例如在发现关联和情节规则，顺序模式和聚类中。但是，频繁项集的数量通常很大。因此，最近提出了许多频繁项集的无损表示。此类表示形式中的两个，即封闭项集和生成器表示形式，特别有用，因为它们可以有效地应用于发现最有趣的非冗余关联和情节规则。另一方面，实验证明，频繁模式的其他表示比这两种表示更简洁，提取速度更快，甚至高出几个数量级。因此，这种简明的表示形式似乎是实现和重用频繁模式知识的有趣替代方法。然而，出现了问题，即如何有效地并且优选地在不访问数据库的情况下将中间表示转换为期望的表示。本文解决了这个问题。由于研究了频繁模式表示的属性，我们提供了一套有效的算法来在它们之间进行无数据转换。

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