Research and Application of Improved Apriori Algorithm Based on Matrix

Abstract

This article put forward a NCM_Apriori algorithm, which through compressing matrix and reducing the scan times to reduce the database I/O overhead, effectively improve the efficiency of association rule mining. At the same time in the process of generating association rules, computation is greatly reduced by using the nature of probability. And applies the algorithm to the mining of students' course selection system, which can provide decision support for colleges and universities.

An improved Apriori algorithm based on influence weight

“…For the classic Apriori algorithm, literature [8] improved algorithm and IW-Apriori algorithm, experiments are carried out in the same experimental environment. Experimental environment: the processor is Intel(R) Core(TM) i 5-7500 CPU @ 3.40 GHz; the operating system is Windows 10 Professional Edition 64 -bit; the algorithm simulation experiment is performed in Python 3.8 platform to achieve.…”

“…Wang C et al proposed an improved Apriori algorithm for Boolean matrix reduction [5][6] , which reduces the number of times and shortens the running time of the algorithm. Shuwen L et al proposed the improved Apriori algorithm based on the compression matrix [7][8][9] , which only needs to scan the database once to generate a Boolean matrix. However, all itemsets are treated equally without distinction.…”

“…At the same time, the compression matrix and weight vector are introduced to represent the transaction database, and the connection strategy of the Apriori algorithm is optimized, and the structure of the matrix is continuously reduced during the operation process. Finally, the improved algorithm is simulated and compared with the classic Apriori algorithm and the compressed matrix-based Apriori algorithm [8] to verify its effectiveness.…”

Clustering Based Approach to Enhance Association Rule Mining

“…The execution time of the Apriori algorithm increases exponentially with an increasing number of transactions. Multiple studies used a parallelization approach using a mapreduce framework to deal with this issue (Liu and Lou;. Another approach that is used to solve this issue is the reduction of the database size.…”

Mining and Correlation Analysis of Association Rules between Properties and Therapeutic Efficacy of Chinese Materia Medica Based on Strategy Pattern

“…Quantization refers that by reasonable analysis the complex CMM data are divided into independent and exclusive minimum information units that consist of several Chinese characters and numbers and cannot be subdivided. [ 3 ] Regarding research subject selection, the authoritative work Pharmacopoeia of the People's Republic of China 2015 edition (hereinafter referred to as Pharmacopoeia) was taken as reference, and the information of 619 CMM recorded in the Pharmacopoeia was extracted. The extracted information included the natures, flavors, channel tropisms, toxicities, therapeutic efficacies, and indications of CMM.…”