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A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives

机译：一种新的有效方法，使用最少的数据结构来挖掘不确定的频繁模式，而不会产生误报

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摘要

The concept of uncertain pattern mining was recently proposed to fulfill the demand for processing databases with uncertain data, and various relevant methods have been devised. However, previous approaches have the following limitations. State-of-the-art methods based on tree structure can cause fatal problems in terms of runtime and memory usage according to the characteristics of uncertain databases and threshold settings because their own tree data structures can become excessively large and complicated in their mining processes. Various approximation approaches have been suggested in order to overcome such problems; however, they are methods that increase their own mining performance at the cost of accuracy of the mining results. In order to solve the problems, we propose an exact, efficient algorithm for mining uncertain frequent patterns based on novel data structures and mining techniques, which can also guarantee the correctness of the mining results without any false positives. The newly proposed list-based data structures and pruning techniques allow a complete set of uncertain frequent patterns to be mined more efficiently without pattern losses. We also demonstrate that the proposed algorithm outperforms previous state-of-the art approaches in both theoretical and empirical aspects. Especially, we provide analytical results of performance evaluation for various types of datasets to show efficiency of runtime, memory usage, and scalability in our method.
机译：最近提出了不确定模式挖掘的概念，以满足处理具有不确定数据的数据库的需求，并且已经设计了各种相关方法。但是，先前的方法具有以下局限性。基于树形结构的最新方法会根据不确定的数据库和阈值设置的特性在运行时和内存使用方面造成致命问题，因为它们自己的树形数据结构在挖掘过程中可能变得过大和复杂。为了克服这些问题，已经提出了各种近似方法。但是，这些方法会以挖掘结果的准确性为代价来提高自身的挖掘性能。为了解决这些问题，我们提出了一种基于新颖的数据结构和挖掘技术的精确，高效的不确定频繁模式挖掘算法，该算法还可以保证挖掘结果的正确性而不会产生误报。新近提出的基于列表的数据结构和修剪技术允许更有效地挖掘一整套不确定的频繁模式，而不会造成模式损失。我们还证明，在理论和经验方面，所提出的算法均优于以前的最新方法。尤其是，我们提供了各种类型数据集的性能评估分析结果，以显示我们的方法的运行时效率，内存使用率和可伸缩性。

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