Method and system for maximum satisfiability solution of Boolean type time sequences

Abstract

The invention relates to a method for maximum satisfiability solution of Boolean type time sequences. The method comprises the steps that a real value table is generated by using a Boolean type time sequence set X corresponding to time sequences Y; association tables are generated to record the frequency of association rules in the real value table; the association tables are screened to obtain aBoolean logic result of the real value table; the maximum satisfiability expression about the Boolean type time sequence set X and the time sequences Y is obtained according to the Boolean logic result. The frequency of searching the whole Boolean type time sequences can be reduced, the calculation complexity can be remarkably reduced, the method appears more essential especially for large-scale time sequences, and meanwhile processing after searching of Boolean type time sequences is also linear, namely little calculation time is needed.

Description

technical field [0001] The invention relates to the field of time series processing and data mining, in particular to association rule mining in time series. Background technique [0002] As an important data object, time series comes from scientific computing, finance, biomedicine and other fields. It has the characteristics of huge data scale, high dimension and continuous update. As time series play an increasingly important role, it is getting more and more attention. Association rule mining between time series is to discover association rules in time series through the method of data mining. In recent years, as the scale of time series has become larger, the calculation time required for association rule mining between time series is also long, especially for real-time association rule mining between time series, the length of calculation time is will be more sensitive, so how to reduce the computational complexity of mining association rules between time series is an...

AI Technical Summary

Problems solved by technology

The space complexity of the FP-growth algorithm depends on the original data. Generally, the storage scale required by the FP-growth algorithm is smaller than the sum of the candidate association items of the Aproiri algorithm, because the inconsistency frequent items are deleted in advance in the process of generating the FP tree. degree requirements of those candidate associations, but in the worst case, the space complexity of the FP tree is still very large, and a large number of pointers in the FP tree need to be stored

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