Clustering Time-Series Medical Databases Based on the Improved Multiscale Matching
Clustering Time-Series Medical Databases Based on the Improved Multiscale Matching
复制标题
基于改进多尺度匹配的时间序列医学数据库聚类
DOI：
10.1007/11425274_63
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
S. Tsumoto
中科院分区：
文献类型：
--
作者：
S. Hirano;S. Tsumoto
本文提出了一种新的方法，称为修改的多尺度匹配，使我们能够多尺度结构比较的不规则采样，不同长度的时间序列，如医疗数据。我们修改了传统的多尺度匹配算法，使它产生的序列相异性，可以进一步用于聚类。主要的改进是：（1）引入了一种新的片段表示法，避免了在高尺度下的收缩问题，（2）引入了一种新的相异性度量，直接反映了序列值的相异性。我们研究了该方法在圆柱形钟形漏斗数据集和慢性肝炎数据集上的有用性。结果表明，所提出的方法产生的相异度矩阵，结合传统的聚类技术，导致成功的聚类合成和真实世界的数据。
This paper presents a novel method called modified multiscale matching, that enable us to multiscale structural comparison of irregularly-sampled, different-length time series like medical data. We revised the conventional multiscale matching algorithm so that it produces sequence dissimilarity that can be further used for clustering. The main improvements are: (1) introduction of a new segment representation that elude the problem of shrinkage at high scales, (2) introduction of a new dissimilarity measure that directly reflects the dissimilarity of sequence values. We examined the usefulness of the method on the cylinder-bell-funnel dataset and chronic hepatitis dataset. The results demonstrated that the dissimilarity matrix produced by the proposed method, combined with conventional clustering techniques, lead to the successful clustering for both synthetic and real-world data.