Time series signifying method based on local feature cluster

Abstract

The invention provides a time series signifying method based on a local feature cluster. The time series signifying method based on the local feature cluster includes that original time series is read; a sliding window procedure is called, original time series is divided into multiple sub time series by utilizing of a sliding window; multiple slopes are adopted to show each sub time series of the original time series; a K mean value clustering algorithm is adopted to achieve clustering of the sub time series; and a corresponding sign identification is given to each clustering result. The time series signifying method based on the local feature cluster can well reduce dimensionality and keep form features of time series, is beneficial to further studying of the times series, and further solves the problems that similarity query, classification, clustering, mode digging and the like are directly conducted on the original time series in the prior, low storage and computational efficiency are caused, accuracy and reliability of an algorithm are effected and the like.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a time series symbolization method based on local feature clustering. Background technique [0002] In the field of data mining, time series data is an important type of data object, which is commonly found in scientific research, commercial applications, traffic control, and industrial production. Through the analysis and research of time series, the inner law of the movement, change and development of things can be revealed, which has important practical significance for people to understand things correctly and make scientific decisions based on them. Due to the characteristics of time series data such as high dimensionality, a large amount of noise interference and non-stationary state, performing similarity query, classification, clustering, pattern mining and other work directly on the original time series will not only cause low storage and computing efficiency but also reduce ...

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Problems solved by technology

[0004] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a time series symbolization method based on local feature clustering, which is used to solve the problem of similarity query, classification, Clustering, pattern mining and other work will cause low storage and computing efficiency, and will affect the accuracy and reliability of the algorithm

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