Computer data mining and clustering method based on time sequence

A technology of data mining and time series, applied in text database clustering/classification, computer components, computing, etc., can solve problems such as inapplicability of mining methods, and achieve the effect of simple method, large amount of data, and high dimensionality

Inactive Publication Date: 2016-06-01
石成富
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Problems solved by technology

Most of the current methods may only show relatively good performance in one aspect, but cannot combine to have a good performance in other aspects.
Obviously, there are still some deficiencies in

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  • Computer data mining and clustering method based on time sequence

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Embodiment Construction

[0020] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention.

[0021] like figure 1 Shown is the specific implementation of a kind of computer data mining clustering method based on time series of the present invention, and its specific implementation steps are:

[0022] Step 1: Input a given sample set X, Y, where X={x 1 ,x 2 ,...,x n}, Y={y 1 ,y 2 ,...,y n};

[0023] Step 2: Denoise and normalize the input sample set;

[0024] Step 3: Calculate the extreme points of the time series X and Y to obtain the extreme point sequences X' and Y';

[0025] Step 4: Perform equal-length processing on the obtained regional extremum point sequences X', Y', and obtain classification sequences X", Y" with length k after equal-length processing;

[0026] Step 5: Calculate the class distance for the processed classification sequences X...

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Abstract

The invention discloses a computer data mining and clustering method based on a time sequence. According to the computer data mining and clustering method, denoising and normalization processing is performed on input sample sets X and Y, and extreme point solving is performed on the processed time sequence so that extreme point sequences X' and Y' are obtained; then isometric processing is performed on X' and Y', and classification sequences X" and Y" with equal length are obtained after isometric processing; then class distance calculation is performed on the processed sequences X" and Y", the two classes of the maximum distance are combined and one class is reduced after combination; then class distance calculation is cyclically performed on the processed classification sequences X" and Y" and two classes of the maximum distance are combined until the number of clusters is equal to preset data and then clustering ends; and finally a clustering result is outputted. According to the method, the time sequence data of high data volume and high dimension can be effectively processed, the method is easy and practicable without depending on concrete sequences, data mining and clustering can be effectively performed, and mass data can be effectively compressed and the main characteristics of the data can be maintained.

Description

technical field [0001] The invention relates to the field of computer data mining technology, in particular to a time series-based computer data mining clustering method. Background technique [0002] With the continuous development of social informatization and the continuous expansion of information technology application fields, more and more data have been accumulated in various application fields, including economics, medical care, construction, and the environment. Since the 1980s, the total amount of data around the world has grown rapidly, even doubling in a few months. However, how to effectively use and analyze these data information and obtain hidden useful information from them has become a A huge challenge. Among these massive data, some data are arranged in time order, and this kind of data is called time series (TimeSeries). There are time series in various application fields. Through in-depth study of these time series, it is of great social significance an...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/30
CPCG06F16/285G06F16/35G06F18/2321G06F18/241
Inventor 李洁孙燕石成富
Owner 石成富
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