TY - GEN

T1 - Implementation algorithm modification maximum standard deviation reduction in graph clustering using matrix complement as input

AU - Kamal, K.

AU - Sugeng, K. A.

AU - Siswantining, T.

N1 - Funding Information:
This paper is funded by Universitas Indonesia 2017 Research Grant No 662/UN2.R3.1/HKP.05.00/2017.
Publisher Copyright:
© 2018 Author(s).

PY - 2018/10/22

Y1 - 2018/10/22

N2 - Clustering can be done by various methods, one of them is a graph clustering. One algorithm that can be used for graph clustering is based on the minimum spanning tree. In this algorithm, we count the value of the standard deviation weights of the vertices between nodes, which why it is called maximum standard deviation reduction (MSDR) algorithm. By using the MSDR algorithm, a cluster of optimal cluster results can be found, and the benefit is we do not have to determine the number of clusters. This method is called unsupervised learning. In MSDR algorithm, the number of clusters will be determined automatically by using polynomial regression. However, in a complex case, it will be difficult to obtain the number of clusters, so that a modification of the algorithm MSDR is proposed and called by MMSDR (Modification of Maximum Standard Deviation Reduction). The modification is done by replacing the polynomial regression to calculate the difference of standard deviation change in the value of the previous calculation. This paper will discuss the results of the implementation of the algorithm MMSDR on the data that come from Indonesia flights by airline X. While the input matrix in the MMSDR is using adjacency matrix, in this work we use matrix complement as the input.

AB - Clustering can be done by various methods, one of them is a graph clustering. One algorithm that can be used for graph clustering is based on the minimum spanning tree. In this algorithm, we count the value of the standard deviation weights of the vertices between nodes, which why it is called maximum standard deviation reduction (MSDR) algorithm. By using the MSDR algorithm, a cluster of optimal cluster results can be found, and the benefit is we do not have to determine the number of clusters. This method is called unsupervised learning. In MSDR algorithm, the number of clusters will be determined automatically by using polynomial regression. However, in a complex case, it will be difficult to obtain the number of clusters, so that a modification of the algorithm MSDR is proposed and called by MMSDR (Modification of Maximum Standard Deviation Reduction). The modification is done by replacing the polynomial regression to calculate the difference of standard deviation change in the value of the previous calculation. This paper will discuss the results of the implementation of the algorithm MMSDR on the data that come from Indonesia flights by airline X. While the input matrix in the MMSDR is using adjacency matrix, in this work we use matrix complement as the input.

KW - graph clustering

KW - matrix complement

KW - maximum standard deviation reduction (MSDR)

UR - http://www.scopus.com/inward/record.url?scp=85056103783&partnerID=8YFLogxK

U2 - 10.1063/1.5064192

DO - 10.1063/1.5064192

M3 - Conference contribution

AN - SCOPUS:85056103783

T3 - AIP Conference Proceedings

BT - Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017

A2 - Yuniati, Ratna

A2 - Mart, Terry

A2 - Anggraningrum, Ivandini T.

A2 - Triyono, Djoko

A2 - Sugeng, Kiki A.

PB - American Institute of Physics Inc.

T2 - 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017

Y2 - 26 July 2017 through 27 July 2017

ER -