Bug Triaging: Right Developer Recommendation for Bug Resolution Using Data Mining Technique

2021 ◽  
pp. 609-618
Author(s):  
B. H. Chaitra ◽  
K. S. Swarnalatha
Keyword(s):  
Data Mining ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Bug Triaging ◽  
Developer Recommendation ◽  
Using Data

scholarly journals Implementation of Hospital Examination Reservation System Using Data Mining Technique

2015 ◽  
Vol 21 (2) ◽  
pp. 95
Author(s):  
Hyo Soung Cha ◽  
Tae Sik Yoon ◽  
Ki Chung Ryu ◽  
Il Won Shin ◽  
Yang Hyo Choe ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Reservation System ◽  
Using Data

scholarly journals Analysis on Insurance Policy and Related Customer Behavior using Data Mining Technique

2016 ◽  
Vol 139 (6) ◽  
pp. 46-47
Author(s):  
M. Ashrafa ◽  
D. Asha ◽  
D. Radha ◽  
M. Sangeetha ◽  
R. Jayaparvathy
Keyword(s):  
Data Mining ◽  
Customer Behavior ◽  
Insurance Policy ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Using Data

scholarly journals Impact of Data Mining Technique in Education Institutions

2015 ◽  
Vol 4 (02) ◽  
pp. 01-07
Author(s):  
Mr. Bhushan Bandre, Ms. Rashmi Khalatkar
Keyword(s):  
Data Mining ◽  
Educational Institution ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Admission Process ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Major Parameter ◽  
Using Data ◽  
Major Decision

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.

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