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Heuristic mining method of optimal comparing sequence mode of free interval constraint

A comparison sequence and heuristic technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of long algorithm execution time, loss of useful sequence patterns, etc., and achieve the effect of accurate results

Inactive Publication Date: 2017-01-18
SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] What the present invention aims to solve is the problem that the user sets inappropriate interval constraints without sufficient prior knowledge, resulting in the loss of useful sequence patterns, exhaustively enumerating all possible interval constraints, resulting in excessively long algorithm execution time

Method used

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  • Heuristic mining method of optimal comparing sequence mode of free interval constraint
  • Heuristic mining method of optimal comparing sequence mode of free interval constraint
  • Heuristic mining method of optimal comparing sequence mode of free interval constraint

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Embodiment

[0035] Firstly, a relevant definition of contrastive sequential pattern mining is given: given a finite symbol set ∑, we call it an alphabet, and any symbol in the alphabet is called an item. An ordered sequence composed of items in Σ is called a sequence, expressed as S=1 ,e 2 ,...,e n >, where e i ∈∑(1≤i≤m) is called an element. We use |S| to denote the length of the sequence S, that is, the number of elements contained in the sequence S. we use S [i] To represent the i-th element in the sequence S (1≤i≤|S|), for two elements S in the sequence S [i] and S [j] (1≤i≤j≤|S|), use gap(S,i,j) to represent the number of elements between the two elements in the sequence S, that is, gap(S,i,j)=j-i -1.

[0036] For any two sequences S' and S", the following conditions are satisfied:

[0037] Condition 1: |S'|≥|S"|, that is, the length of the sequence S' is not less than the length of the sequence S";

[0038] Condition 2: There is a set of numbers 1≤k 1 ≤k 2 ≤…≤k |S”| ≤|S'...

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Abstract

The invention discloses a heuristic mining method of an optimal comparing sequence mode of free interval constraints. The heuristic mining method comprises the following steps of: step S1, inputting a positive example sequence set, a negative example sequence set and the expected mining quantity of comparing sequence modes; step S2, randomly generating gene type candidate mode codes with the pre-set quantity; step S3, obtaining a candidate comparing sequence mode corresponding to each gene type candidate mode code; step S4, calculating the contrast ratio of each candidate comparing sequence mode; step S5, judging whether the current gene type candidate mode codes meet method finishing conditions or not; if so, taking the k candidate comparing sequence modes with the optimal contrast ratio as a final mining result; otherwise, executing step S6; step S6, selecting the current gene type candidate mode codes; and step S7, forming new gene type candidate mode codes and returning to the step S3. By adopting the comparing sequence mode mining method provided by the invention, a condition that a result is lost due to improper parameter setting under the condition that a user does not have priori knowledge is avoided.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a heuristic mining method for optimal comparison sequence patterns with free interval constraints. Background technique [0002] As an important task of data mining, sequential pattern mining has a wide range of applications. For example, power companies improve the accuracy of power load forecasting by analyzing historical power consumption data. For another example, by analyzing the spatio-temporal monitoring data of infectious disease transmission, the health and disease control department is expected to discover the spatio-temporal cluster outbreak pattern of infectious diseases, and then provide reference for prevention and control work. At the same time, sequential pattern mining has also attracted the attention of many researchers, and different types of sequential patterns have been proposed, such as frequent sequential patterns, closed sequential patterns, periodic ...

Claims

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Application Information

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IPC IPC(8): G06F19/24
CPCG16B40/00
Inventor 段磊高超杨皓王慧锋
Owner SICHUAN UNIV
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