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Self-adaptive high-average utility sequence pattern mining method under one-time condition
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A one-time, self-adaptive technology, applied in data mining, special data processing applications, instruments, etc., can solve problems such as difficulty in solving generality, accuracy and flexibility, and inability to mine valuable information
Pending Publication Date: 2020-07-31
HEBEI UNIV OF TECH
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[0024] The technical problem to be solved by the present invention is to provide an adaptive high-average-utility sequential pattern mining method under one-time conditions, by setting the support lower bound, using pattern splicing to reduce the space of candidate patterns, and adopting a queue structure to calculate the support in the pattern to solve once Adaptive high-average utility sequence pattern mining under one-off conditions, this method adaptively mines high-average utility patterns under one-off conditions, overcomes the existing problems in the prior art for adaptive high-average utility sequence pattern mining under one-off conditions It is difficult to take into account the generality, accuracy and flexibility of the solution, and the defect that valuable information cannot be mined while ensuring that the result set is not redundant
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Embodiment 1
[0098] Given a piece of DNA sequence S=s 1 the s 2 the s 3 the s 4 the s 5 the s 6 the s 7 the s 8 the s 9 the s 10 =ACGACGACGG, the average utility threshold minunity=20, the utility values of each item are: U(A)=10, U(C)=4, U(G)=7.
[0099] The first step is to read the sequence database SDB, the average utility threshold minunity, and the utility value U(P) of each item:
[0100] Read in a given sequence database SDB, determine its size as N, and record each sequence in the sequence database SDB as sequence S 1 , sequence S 2 , ..., sequence S k , ..., sequence S N , where 1≤k≤N, the sequence S k Each character in is denoted as the character s 1 , character s 2 , ..., character s i ..., the character s n , given the average utility threshold minunity, the utility value U(P) of each item;
[0101] The concrete operation of this embodiment is as follows:
[0102] Read into the given sequence database SDB, which contains a sequence S=ACGACGACGG, the averag...
Embodiment 2
[0319] Given a piece of DNA sequence S=s 1 the s 2 the s 3 the s 4 the s 5 the s 6 =ACACAG, the average utility threshold minunity=20, the utility values of each item are: U(A)=10, U(C)=4, U(G)=7.
[0320] In addition to the ninth step "when the above sixth step obtains a high lower bound mode set Hcand with a mode length of m+1 m+1 When it is empty, the high average utility pattern has been excavated " except that other is the same as embodiment 1.
[0321] Because in step 6 a high lower bound pattern set of pattern length 3 is generated is empty, so the high average utility mode is mined.
[0322] In the foregoing embodiment, the programmingsoftware used is VC++6.0, and the drawing tool is Visio2013, and the processor used is Inter(R) Core(TM) i5-5200U CPU@2.2.GHz , the operating system is Windows 7 and above, and the software and hardware environments used above are well known to those skilled in the art.
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Abstract
The invention discloses a self-adaptive high-average utility sequence pattern mining method under a one-time condition. The invention relates to the technical field of electric digital dataprocessing. A support degree lower bound is set, the space of candidate patterns is reduced by using pattern splicing, and the support degree in each pattern is calculated by using a queue structure to solve the problem of self-adaptive high-average utility sequence pattern mining under a one-time condition. A high-average utility pattern is adaptively mined under a one-time condition in the method, and thedefects that in the prior art, it is difficult to achieve the solving universality, accuracy and flexibility for the mining of a self-adaptive high-average utility sequence pattern under a one-time condition, and valuable information cannot be mined while non-redundancy of a result set is guaranteed are overcome.
Description
technical field [0001] The technical proposal of the present invention relates to the technical field of electric digital dataprocessing, in particular to an adaptive high average utility sequential pattern mining method under one-time conditions. Background technique [0002] With the rapid development of computer and information technology, the Internet is constantly permeating all areas of people's life and work, accompanied by the generation and storage of massive data, how to mine potentially useful information from large amounts of data has become a research hotspot. People not only hope to extract valuable information from big data, but also hope to discover deeper laws that can effectively support decision-making in production and life. With the development of the Internet, sequential pattern mining has become an important research direction in computers. Sequential pattern mining has a wide range of applications, not only in the biological field, but also in the c...
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