扩展的编辑距离度量
The Extended Edit Distance Metric
摘要：
相似性搜索是信息检索中的一个重要问题。这种相似性基于距离。时间序列的符号化表示由于能够降低高维数据对象的维数，近年来吸引了众多的研究者。我们提出了一个新的距离度量，适用于符号数据对象，我们测试它的时间序列数据库中的分类任务。我们比较它与其他距离是众所周知的，在文献中的符号数据对象。我们还证明，数学上，我们的距离是公制。
英文摘要：
Similarity search is an important problem in information retrieval. This similarity is based on a distance. Symbolic representation of time series has attracted many researchers recently, since it reduces the dimensionality of these high dimensional data objects. We propose a new distance metric that is applied to symbolic data objects and we test it on time series data bases in a classification task. We compare it to other distances that are well known in the literature for symbolic data objects. We also prove, mathematically, that our distance is metric.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
