Periodic associated rule discovery algorithm based on time sequence vector diverse sequence method clustering

Abstract

The utility model relates to a discovering algorithm with clustered cycling associated rule, based on a differing sequence method of time series vector. Firstly, in view of the drawback of the current discovering algorithm with cycling associated rule on the problem of dividing a plurality of time domains, an algorithm called CMDSA is proposed. The algorithm selects a time series vector which comprises a item supporting degree as the data character in time area to cluster; meanwhile, the clustering number is controlled by a DB principle to reach the best clustering result, so that each time area under the cycling associated rule can be identified more accurately and more useful cycling associated rules can be found compared with the current algorithm. Aiming at the fact that all the current algorithm of cycling associated rule are based on the Apriori algorithm and the efficiency is low, an algorithm of CFP-tree based on Fp tree is proposed. The algorithm of CFP-tree adopts cycling tailoring technique based on the condition FP tree to enhance the algorithm efficiency. Thus, the adoption of the discovering algorithm with cycling associated rule of CFP-tree is far better than the prior algorithm based on Apriori in the time and space efficiency.

Description

1 technical field [0001] The present invention relates to an algorithm for discovering periodic association rules of time series in the field of data mining; in particular, it relates to a class of periodic temporal association rules between attribute states based on temporal constraints, which is suitable for developing a limited number of attributes The problem of associativity of states periodically by time. The temporal association rules of equivalent event mapping, non-identical attributes and identical attributes are defined, and the extraction of temporal association rules is determined by calculating the support rate and credibility. While confirming the effectiveness of temporal association rules, the main steps of the algorithm for mining periodic association rules of time series are given. 2 background technology [0002] Changes in the real world are inseparable from time factors, so studying the periodic association rules of temporal data in real world data can...

AI Technical Summary

Problems solved by technology

[0005] Problem 1: Selection of time-domain data feature points

[0009] Question 2: Determination of the number of segments in the cycle time area

[0011] Question 3: The choice of the basic algorithm for discovering periodic association rules

[0012] arts [2][3][4][5][21] Periodic association rule algorithms are based on Apriori algorithm [6] , and text [1] Algorithms and Documentation for Discovering Association Rules with Temporal Constraints [18] The partial periodic pattern mining algorithm is also based on the Apriori algorithm [6] , they all have a very large set of candidate items to be processed, it will take a lot of time to search and match patterns and database transactions, high resource consumption, low operating efficiency, etc.

Images