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Similarity measuring algorithm for sequences in different lengths

A similarity, long sequence technology, applied in the field of data fusion algorithms, can solve problems such as transformation errors, affecting the similarity measurement between sequences, and reducing the authenticity of data associations

Inactive Publication Date: 2014-08-13
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Problems solved by technology

This kind of method is easy to introduce transformation error when transforming sequence data, which affects the similarity measurement between sequences, thereby reducing the authenticity of data association.

Method used

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  • Similarity measuring algorithm for sequences in different lengths
  • Similarity measuring algorithm for sequences in different lengths
  • Similarity measuring algorithm for sequences in different lengths

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Embodiment

[0061] Assuming that two types of sensors sensor1 and sensor2 are set as ESM and ELINT, the target is measured, and three types of target identity information are measured, radar carrier frequency RF, pulse repetition frequency PRF and pulse width PW, which are obtained after front-end data processing and correlation Two target sequence matrices S to be identified 1 and S 2 , which are composed of three bar sequences, representing RF, PRF and PW three types of parameters. There are four types of target identity attributes in the target database, and the sequence matrix Q 1 , Q 2 , Q 3 and Q 4 Indicates that the data parameters in the matrix and the target sequence matrix S to be identified are 1 and S 2 Correspondingly, the lengths between them are unequal. Adopt the method proposed by the present invention to calculate two target sequence matrices S to be identified 1 and S 2 and four types of target identity attribute sequence matrix Q 1 , Q 2 , Q 3 and Q 4 simi...

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Abstract

The invention discloses a similarity measuring algorithm for sequences in different lengths, in order to solve the similarity measuring problem of sequences in different lengths. The similarity measuring algorithm comprises the following steps: constructing a sliding window to traverse the sequences in different lengths; taking a sequence in short length as the sliding window; successively sliding for a unit length along the sequence in longer length till traversing the sequence in the whole length; calculating the similarity of the corresponding sequences in equal length during each traversing process, thereby forming sliding similarity; weighting and combing the similarity of all sliding areas through an optimal weight vector, thereby acquiring the similarity measure of the sequences in different lengths. According to the method, the similarity error is reduced and the adaptive control for similarity weighting is realized.

Description

technical field [0001] The invention relates to a data fusion algorithm, in particular to a data mining algorithm for unequal-length sequences. Background technique [0002] As a kind of uncertain data, sequence data is the main research object in the field of data mining, and widely exists in the fields of economic forecasting, medical research, weather forecasting, network security and military science. With the rapid development of information technology, the amount of data is increasing, and the information contained is also increasing, which undoubtedly entered the era of big data. How to mine effective information and knowledge hidden in these data has been extensively studied in recent years. Sequence data is high-dimensional data composed of many data points. The length of these data points may be inconsistent with time. Mining these sequence data with inconsistent length is a key issue in data mining. Sequence similarity measurement method is an important process ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 关欣孙贵东宋瑞华赵志勇衣晓
Owner 关欣
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