Modification of A priori Algorithm focused on confidence value to association rules

2nd Nommensen International Conference on Technology and Engineering

Ginting, Dewi Sartika^1 ; Mawengkang, Herman^2 ; Efendi, Syahril^3

Stud. in Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia^1

Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Medan, Indonesia^2

Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia^3

关键词: Average difference;    Bayesian methods;    Candidate generation;    Confidence values;    Data mining algorithm;    Extraction of information;    Minimum support;    Minimum value;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/420/1/012125/pdf
DOI  :  10.1088/1757-899X/420/1/012125

来源: IOP

【 摘 要 】

A priori algorithm is one of the data mining algorithm in formation of rule mining association. A priori algorithm is the process of extraction of information from a database, followed by frequent item / itemset and candidate generation in formation of association rule mining in order to obtain minimum value of support and minimum confidence value. The value of confidence has a big effect on rule the resulting, where the rule generated by the k-item sets pattern needs to be calculated on the level of confidence or certainty of the k-item sets pattern that has complied with the rules. Therefore, this research discusses about a priori algorithm modification which focuses on giving confidence value for each rule generated. Modifications are made by substituting the Bayesian method on a standard A priori confidence formula. And for the next process there is difference of confidence value between a priori standard and modification, where the value of confidence generated a priori modification is bigger, and after calculated for some rules taken according to minimum support condition then there is average difference of confidence value equal to 10,50%.