TY - GEN
T1 - Attribute Selection Techniques to Clustering the Entrepreneurial Potential of Student based on Academic Behavior
AU - Rijati, Nova
AU - Sumpeno, Surya
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - A key factor in the process of knowledge discovery in databases is the quality of data that consists of a set of attributes that explain the characteristics of the data. For that, we need the right attribute selection method for optimal data mining performance. In this case, the attributes tested with machine learning are the result of mapping factors is affecting entrepreneurship of students based on behavioral science theory on the attributes of Indonesia Higher Education Database. Testing dataset attributes using four different methods, namely Correlation, Information Gain, OneR, and Relief F. The results of clustering experiments with the Simple K-Means algorithm show that OneR method decrease in the largest drop of Sum of Squared Errors (17%) compared to the other three methods. With the most important attribute differences in each attribute selection method, the instances cluster profile generated is also different.
AB - A key factor in the process of knowledge discovery in databases is the quality of data that consists of a set of attributes that explain the characteristics of the data. For that, we need the right attribute selection method for optimal data mining performance. In this case, the attributes tested with machine learning are the result of mapping factors is affecting entrepreneurship of students based on behavioral science theory on the attributes of Indonesia Higher Education Database. Testing dataset attributes using four different methods, namely Correlation, Information Gain, OneR, and Relief F. The results of clustering experiments with the Simple K-Means algorithm show that OneR method decrease in the largest drop of Sum of Squared Errors (17%) compared to the other three methods. With the most important attribute differences in each attribute selection method, the instances cluster profile generated is also different.
KW - academic behavior
KW - attribute selection
KW - clustering
KW - entrepreneurial potential
UR - http://www.scopus.com/inward/record.url?scp=85084633495&partnerID=8YFLogxK
U2 - 10.1109/CIVEMSA45640.2019.9071597
DO - 10.1109/CIVEMSA45640.2019.9071597
M3 - Conference contribution
AN - SCOPUS:85084633495
T3 - 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2019 - Proceedings
BT - 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th Annual IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2019
Y2 - 14 June 2019 through 16 June 2019
ER -