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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • graph clustering
  • matrix complement
  • maximum standard deviation reduction (MSDR)

Fingerprint Dive into the research topics of 'Implementation algorithm modification maximum standard deviation reduction in graph clustering using matrix complement as input'. Together they form a unique fingerprint.

Cite this