Periodic gap constrained negative sequence pattern mining method

A pattern mining and negative sequence technology, applied in special data processing applications, instruments, knowledge expression, etc., can solve problems such as inability to take into account negative sequences

CN113076357AInactive Publication Date: 2021-07-06HEBEI UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2021-07-06
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to a periodic gap constrained negative sequence pattern mining method, which comprises the following steps of: generating a candidate pattern by adopting a negative sequence pattern growth strategy, simultaneously considering a positive element and a negative element during pattern growth, and proposing a constraint on a pattern participating in pattern growth; adopting an incomplete network tree structure to help to calculate the support degree of the candidate mode in the sequence, and calculating the support degree of the candidate mode by using incomplete network tree arrays of a prefix and a suffix of the candidate mode; wherein the candidate mode with the given length of k+1 and the mode with the length of k are prefixes; the mode with the length of k is a suffix; all prefixes with the length of k are stored in a prefix set PreArrk, and all suffixes with the length of k are stored in a suffix set SefArrk. The mode growth and the incomplete network tree strategy are used, so that the understanding completeness is ensured, and the space-time efficiency of the algorithm is improved.
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Description

technical field

[0001] The technical solution of the invention relates to the field of sequence pattern analysis, in particular to a negative sequence pattern mining method with periodic gap constraints. Background technique

[0002] With the rapid development of artificial intelligence, data is playing an increasingly important role in our lives, and countless sequence data have emerged in many fields, such as biometric information mining, group behavior analysis, malicious network attack investigation, customer Purchase information analysis, etc. These data are huge in size and have multi-category and multi-dimensionality. How to quickly and effectively extract valuable information from these data has become a hot spot in current scientific research. In order to discover these valuable information, sequential pattern mining came into being, and as an important branch of data mining, it has been widely used. However, sequential pattern mining only focuses on appearing ele...

Examples

Embodiment 1

[0117] Given a sequence database D={s 1 = d 1 d 2 d 3 d 4 d 5 d 6 d 7 d 8 d 9 d 10 =ttcctccgcg,s 2 = d 1 d 2 d 3 d 4 d 5 =tggct}, given the density threshold ρ=0.1, the minimum gap constraint M=0, and the maximum gap constraint N=2.

[0118] The first step is to read the sequence database D given by the user, the threshold ρ and the period gap constraint [M,N]:

[0119] Read into the sequence database D, it can be seen that D has a total of g sequences, and each sequence is recorded as sequence s 1 , sequence s 2 , ..., sequence s g , where the sequence s i The elements contained in (1≤i≤g) are respectively denoted as element d 1 , element d 2 , ..., element d l , that is, the length is l, read in D to get h different elements, and get the element set E={e 1 、e 2 ...e h }. Read in the threshold ρ, and read in the periodic gap constraint [M,N].

[0120] The specific processing method is as follows:

[0121] Read int...