MASS:查询在时间序列中的距离分布图

IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Data Mining and Knowledge Discovery Pub Date : 2024-02-05 DOI:10.1007/s10618-024-01005-2
Sheng Zhong, Abdullah Mueen
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引用次数: 0

摘要

在给定一个长时间序列的情况下,查询时间序列的距离轮廓计算的是查询时间序列与长时间序列的每个可能子序列之间的距离。MASS(Mueen's Algorithm for Similarity Search)是一种在z归一化欧氏距离下高效计算距离剖面的算法(Mueen等人在The fastest similarity search algorithm for time series subences under Euclidean distance. http://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html,2017)。在许多数据挖掘工作中,MASS 是公认的有用工具。然而,关于该算法日益高效版本的完整文档并不存在。在本文中,我们正式定义了距离剖面的概念,描述了 MASS 算法的四个版本,展示了距离剖面在各种操作条件下的若干扩展,描述了 MASS 如何提高现有数据挖掘算法的性能,最后展示了 MASS 在地震学、机器人学和电网等领域的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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MASS: distance profile of a query over a time series

Given a long time series, the distance profile of a query time series computes distances between the query and every possible subsequence of a long time series. MASS (Mueen’s Algorithm for Similarity Search) is an algorithm to efficiently compute distance profile under z-normalized Euclidean distance (Mueen et al. in The fastest similarity search algorithm for time series subsequences under Euclidean distance. http://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html, 2017). MASS is recognized as a useful tool in many data mining works. However, complete documentation of the increasingly efficient versions of the algorithm does not exist. In this paper, we formalize the notion of a distance profile, describe four versions of the MASS algorithm, show several extensions of distance profiles under various operating conditions, describe how MASS improves performances of existing data mining algorithms, and finally, show utility of MASS in domains including seismology, robotics and power grids.

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来源期刊
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery 工程技术-计算机:人工智能
CiteScore
10.40
自引率
4.20%
发文量
68
审稿时长
10 months
期刊介绍: Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.
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