Order-preserving sequence pattern mining method

A sequential pattern mining and pattern technology, applied in data mining, special data processing applications, instruments, etc., can solve problems such as difficulty in key trend analysis, loss of important information, difficulty, etc.

Inactive Publication Date: 2020-08-25
HEBEI UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0012] The technical problem to be solved by the present invention is to provide a sequential pattern mining method that preserves the order, use the pattern fusion method to generate candidate patterns, reduce the number of candidate patterns, calculate the pattern support of candidate patterns through a series of conversion and verification steps, and overcome the In view of the existing technology for mining frequent patterns from time series, it is difficult to take into account the accuracy, generality and completeness of the solution, it is difficult to ensure that important information is not lost when processing time series, and it is difficult to mine frequent patterns. The pitfalls of using patterns to analyze key trends

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Effect test

Embodiment 1

[0073] Given a time series S=(1.1, 1.2, 1.3, 1.4, 1.5, 1.1, 1.2, 1.3, 1.4, 1.5, 1.1, 1.2, 1.3, 1.4, 1.5, 1.3, 1.4), the minimum support threshold minsup=3.

[0074] In the first step, input the time series S and the minimum support threshold minsup:

[0075] Input time series S=(1.1,1.2,1.3,1.4,1.5,1.1,1.2,1.3,1.4,1.5,1.1,1.2,1.3,1.4,1.5,1.3,1.4), and minimum support threshold minsup=3;

[0076] The second step is to obtain the frequent pattern set fre with a pattern length of 2 2 :

[0077] The candidate pattern set cand with a pattern length of 2 2 ={(1,2),(2,1)}, according to the calculation steps of the pattern support as described below, sequentially calculate the candidate pattern set cand with a pattern length of 2 2 = the mode support of each candidate mode in {(1,2), (2,1)} in the time series S, when the mode support of the candidate mode ≥ the minimum support threshold minsup, the candidate mode P d is a frequent pattern with a pattern length of 2, and the candid...

Embodiment 2

[0185] Given a time series S=(2,1,3,4,8,9,7,12,14,13,15,17), the minimum support threshold minsup=3.

[0186] "The fifth step, when the candidate pattern set cand with pattern length L+1 L+1 If it is empty, the order-preserving sequence pattern has been mined.

[0187] Because in the third step, the set of candidate patterns with a pattern length of 5 The candidate pattern set cand with a pattern length of 5 5 is empty, so the order-preserving sequence pattern mining is complete. "

[0188] Except above-mentioned difference, other is with embodiment 1.

[0189] In the foregoing embodiment, the programming software used is VC ++ 6.0, and the drawing tool is Visio2013, and the processor used is Pentium(R) Dual-Core 32Processor+, and the operating system is Windows7 and above versions, classic pattern matching algorithm, the above software used and hardware environment are well known to those skilled in the art.

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Abstract

The invention discloses an order-preserving sequence pattern mining method and relates to the technical field of electric digital data processing. According to the method, a candidate mode is generated by using a mode fusion method; the number of candidate modes is reduced, and the mode support degree of the candidate modes is calculated through a series of conversion and verification steps. The defects that in the prior art, for mining frequent patterns from a time sequence, solving accuracy, generality and completeness are difficult to consider at the same time, it is difficult to ensure that important information is not lost when the time sequence is processed, and it is difficult to analyze key trends by mining the frequent patterns are overcome.

Description

technical field [0001] The technical solution of the present invention relates to the technical field of electrical digital data processing, in particular to a method for mining order-preserving sequence patterns. Background technique [0002] Sequential pattern mining has become one of the important tasks in data mining. It is widely used in sequence analysis, classification and forecasting. Its task is to find frequent patterns in massive sequence data. At present, sequence data is usually divided into two types: character sequence and time series. Common character sequences include DNA sequences, protein sequences, etc. Mining frequent patterns in them can help people solve problems in biology; time series is numerical data that is measured and recorded over time, such as daily stock prices, oil production , daily temperature, etc. are common time series, and it is not meaningful to simply focus on the magnitude of their values. People are more interested in the trends p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06N5/02
CPCG06F16/2465G06F2216/03G06N5/025
Inventor 武优西户倩郭媛王晓慧赵晓倩王珠林崔文峰
Owner HEBEI UNIV OF TECH
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