Symbolic pattern recognition for sequential data
Symbolic pattern recognition for sequential data
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DOI：
10.1080/07474946.2017.1394719
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发表时间：
2017-01-01
影响因子：
0.8
通讯作者：
Howe, J. Andrew
中科院分区：
文献类型：
--
作者：
Akbilgic, Oguz;Howe, J. Andrew
顺序数据源围绕并渗透我们的生活。我们的身体不断产生连续的数据，如心率和血压。在全球金融中，股票指数和货币汇率每秒钟都在变化。云的运动，行星的坐标，足球比赛的比分，等等，都是顺序数据以规则的时间步长从一个状态转换到另一个状态的例子。有一个成熟的文献机构与建模时间相关的顺序数据，或时间序列，其中每个模型依赖于特定的假设适用性，数据。然而，还有其他种类的顺序数据不是相对于时间收集的，例如DNA序列（如果我们忽略基因突变）。这种类型的顺序数据的建模没有一个成熟的文献。这是有点令人困惑，因为顺序数据modelingshould涵盖时间相关和非时间相关的data.In这项研究中，我们引入了一个框架，称为符号模式识别建模模式的过渡行为的顺序数据，是由一个有限的字母表的符号。我们的框架可以用来描述，预测，模拟，聚类和分类多个系列的基础上观察到的模式transitionbehaviors.We文件我们提出的框架，并应用它来执行无监督聚类的13种不同的软体动物的基础上，他们的DNA序列。我们的模型正确地将软体动物聚类到各自的属中，与通过更传统的监督遗传分析获得的结果相匹配。
Sources of sequential data surround and pervade our lives. Our bodies continuously generate sequential data such as heart rate and blood pressure. In global finance, stock indices and currency exchange rates change every second. The movement of clouds, the coordinates of the planets, the score of a soccer game, etc., are all examples of sequential data transitioning fromone state to another in regular time steps. There a mature body of literature related to modeling time-dependent sequential data, or time series, in which every model relies upon specific assumptions for applicability, to data. However, there other kinds of sequential data that are not collected with respect to time., for example DNA sequences (if vve ignore the gene mutations). Modeling of this type of sequential data does not have a body of literature that is as mature. This is somewhat perplexing, since sequential data modelingshould cover both time-dependent and non-time-dependent data.In this study, we introduce a framework called Symbolic Pattern Recognition for modeling pattern transition behavior of sequential data that is expressed by a finite alphabet of symbols. Our framework can be used to characterize, predict, simulate, cluster, and classify multiple series based on their observed pattern transition behaviors.We document our proposed framework and apply it to perform unsupervised clustering of 13 different species of mollusks based on their DNA sequences. Our model correctly clusters the mollusks into their respective genera, matching results obtained via more traditional, supervised genetic analysis.