scholarly journals Research On QAR Data Mining Method Based On Improved Association Rule

2012 ◽  
Vol 24 ◽  
pp. 1514-1519 ◽  
Author(s):  
Qiao yongwei ◽  
Yang Hui ◽  
Dong Tingjian
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Mining Method ◽  
Data Mining Method

Applying Association Rule of the Data Mining Method for the Network Event Analysis

2007 ◽  
Author(s):  
Wankyung Kim ◽  
Wooyoung Soh ◽  
Theodore E. Simos ◽  
George Maroulis
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Event Analysis ◽  
Mining Method ◽  
Data Mining Method ◽  
Network Event

scholarly journals Using Data Mining Algorithms to Discover Regular Sound Changes among Languages

2019 ◽  
Vol 292 ◽  
pp. 03018
Author(s):  
Peter Z. Revesz
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Sound Change ◽  
Mining Method ◽  
Data Mining Method ◽  
Data Mining Algorithms ◽  
Sound Changes ◽  
Using Data ◽  
Mining Algorithms

This paper presents a method of using association rule data mining algorithms to discover regular sound changes among languages. The method presented has a great potential to facilitate linguistic studies aimed at identifying distantly related cognate languages. As an experimental example, this paper presents the application of the data mining method to the discovery of regular sound changes between the Hungarian and the Sumerian languages, which separated at least five thousand years ago when the Proto-Sumerian reached Mesopotamia. The data mining method discovered an important regular sound change between Hungarian word initial /f/ and Sumerian word initial /b/ phonemes.

scholarly journals Model Prediksi Penempatan Magang Siswa SMK menggunakan Teknik Association Rule Mining

2020 ◽  
Vol 9 (1) ◽  
pp. 1-10
Author(s):  
Dwi Welly Sukma Nirad ◽  
Afriyanti Dwi Kartika ◽  
Aghill Tresna Avianto ◽  
Aulia Anshari Fathurrahman
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Association Rule ◽  
Association Rule Mining ◽  
Vocational School ◽  
Mining Method ◽  
Rule Mining ◽  
The Core ◽  
Data Mining Method ◽  
The Future

Insternship activity is one of the core activities of every Vocational School (SMK) as the purpose of this school is to conduct education at the level of work-oriented readiness. Every SMK graduate is expected to be better prepared to enter the industrial world. However, in fact there were gaps that resulted in the unpreparedness of students after graduating from school. This research identified and analyzed the placement of student internships. The aim was to find an insternship placement pattern in order to get an overview and recommendation of an appropriate internship according to students abilities. The technique used was the association rule mining, a technique of the data mining method that was useful for uncovering the rules that were correlated to each other so that they can better organize and predict the internship placements. The results showed that the association rule mining could be applied to analyze student performance and predict internship placements in the future. This prediction could be a consideration for the teacher to determine the subjects that need to be improved to prepare students for internships.

Sign in / Sign up

Export Citation Format

Share Document