顺序模式挖掘的抽样:从静态数据库到数据流

Chedy Raïssi, P. Poncelet
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引用次数: 30

摘要

顺序模式挖掘是知识发现领域的一个活跃领域。最近,随着硬件技术的不断进步,现实世界的数据库越来越大,可以将数据库加载到主存中进行顺序模式挖掘的假设不再有效。此外,新的数据模型是一个连续的、可能无限的流,称为数据流模型,需要预处理步骤来简化挖掘操作。由于数据库大小是对挖掘算法影响最大的因素,我们研究了在静态数据库上使用抽样来获得具有错误率上界的近似挖掘结果。此外,我们扩展了这些采样分析,并提出了一种基于储层采样的算法来处理数据流上的顺序模式挖掘。我们用实证结果证明了我们的抽样方法是有效的,序列挖掘在静态数据库和数据流上仍然是准确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sampling for Sequential Pattern Mining: From Static Databases to Data Streams
Sequential pattern mining is an active field in the domain of knowledge discovery. Recently, with the constant progress in hardware technologies, real-world databases tend to grow larger and the hypothesis that a database can be loaded into main-memory for sequential pattern mining purpose is no longer valid. Furthermore, the new model of data as a continuous and potentially infinite flow, known as data stream model, call for a pre-processing step to ease the mining operations. Since the database size is the most influential factor for mining algorithms we examine the use of sampling over static databases to get approximate mining results with an upper bound on the error rate. Moreover, we extend these sampling analysis and present an algorithm based on reservoir sampling to cope with sequential pattern mining over data streams. We demonstrate with empirical results that our sampling methods are efficient and that sequence mining remains accurate over static databases and data streams.
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