Periodic gap constrained negative sequence pattern mining method

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.

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...

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Problems solved by technology

[0009] Aiming at the deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a negative sequence pattern mining with periodic gap constraints, which can dig out negative sequence patterns that meet the periodic gap constraints from the sequence, and this method overcomes the current gap constraints. The problem of negative sequences cannot be taken into account in the pattern mining under

In order to solve the problem of computing pattern support mentioned above, this paper uses an incomplete network tree structure to help calculate the support of candidate patterns in the sequence, which is different from other methods that use incomplete network trees that only use an incomplete network tree Scan the database to calculate the support of a certain pattern. This method uses the incomplete network tree of the candidate pattern prefix and suffix to calculate the support of the candidate pattern. The advantage of this method is that it does not need to scan the database repeatedly and has higher time efficiency.

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