Numerical Time-Series Pattern Extraction Based on Irregular Piecewise Aggregate Approximation and Gradient Specification
Numerical Time-Series Pattern Extraction Based on Irregular Piecewise Aggregate Approximation and Gradient Specification
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DOI：
10.1007/s00354-007-0013-9
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发表时间：
2007
影响因子：
2.6
通讯作者：
M. Ohsaki;H. Abe;Takahira Yamaguchi
中科院分区：
文献类型：
--
作者：
M. Ohsaki;H. Abe;Takahira Yamaguchi
本文提出并评价了一种考虑用户主观性的从数值时间序列数据中提取兴趣模式的方法。该方法使用用户指定的梯度对数据进行不规则采样，保留主观上值得注意的特征。它还进行不规则量化，利用统计分布保留数据的内在客观特征。然后利用群平均聚类从离散数据中提取有代表性的模式。基于基准数据集的实验结果表明，该方法与基于K-Means算法的基本子序列聚类具有相同的性能，没有破坏固有的客观特征。使用临床肝炎研究数据集的结果表明，它为医学专家提取了有趣的模式。
This paper proposes and evaluates a method for extracting interesting patterns from numerical time-series data which takes account of user subjectivity. The proposed method conducts irregular sampling on the data preserving the subjectively noteworthy features using a user specified gradient. It also conducts irregular quantization, preserving the intrinsically objective characteristics of the data using statistical distributions. It then extracts representative patterns from the discretized data using group average clustering. Experimental results using benchmark datasets indicate that the proposed method does not destroy the intrinsically objective features, since it has the same performance as the basic subsequence clustering using K-Means algorithm. Results using a dataset from a clinical hepatitis study indicate that it extracts interesting patterns for a medical expert.