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Mining approximate sequential patterns with gaps

机译：挖掘带有间隙的近似顺序模式

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摘要

Time series data are found in diverse fields including, science, business, medicine and engineering. In this paper, we consider sequential pattern mining for categorical time series data that contain multiple independent time-series. Frequent patterns are considered important in a variety of applications. However, it is common for data to contain noise, and/or for the source process to have considerable variability. Conventional sequential pattern mining methods that use exact matching address, some but not all of these difficulties. Two general approaches used in previous studies to mine sequential patterns in data with noise are distance-based clustering and hidden Markov models. While these approaches are useful in mining frequent sequential patterns in noisy data, we further propose a framework (MWASP: multiple-width approximate sequential pattern mining) that uncovers frequent approximate sequential patterns with various widths. A mined pattern in this framework is representative of a group of sequences that follow the pattern's event flow order. This gives insight into the occurrence of the pattern longitudinally, as well as across the population. The pattern can be recognised as a common pattern across the multiple time series, time, or both.
机译：时间序列数据可在科学，商业，医学和工程学等各个领域找到。在本文中，我们考虑对包含多个独立时间序列的分类时间序列数据进行顺序模式挖掘。在许多应用程序中，频繁模式被认为很重要。但是，数据包含噪声和/或源过程具有很大的可变性是很常见的。传统的顺序模式挖掘方法使用精确的匹配地址，但有些困难而不是全​​部困难。在以前的研究中，用于挖掘具有噪声的数据中的顺序模式的两种通用方法是基于距离的聚类和隐马尔可夫模型。虽然这些方法可用于在嘈杂的数据中挖掘频繁的顺序模式，但我们进一步提出了一个框架（MWASP：多宽度近似顺序模式挖掘），该框架揭示了各种宽度的频繁的近似顺序模式。此框架中的挖掘模式表示遵循模式事件流顺序的一组序列。这样可以洞悉纵向分布以及整个种群中的分布情况。可以将模式识别为跨多个时间序列，时间或两者的通用模式。

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