Improved quantum clustering analysis based on the weighted distance and its application
Cluster analysis is widely used in fields such as economics, management and engineering. The distance and correlation are two of the most important and often used mathematics- and statistics-based similarity measures in cluster analysis. Many studies have been conducted to improve the distance and s...
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doaj-eb170f94ae084fd89e09d1202aa3afc32020-11-25T03:46:41ZengElsevierHeliyon2405-84402018-11-01411e00984Improved quantum clustering analysis based on the weighted distance and its applicationFan Decheng0Song Jon1Cholho Pang2Wang Dong3CholJin Won4School of Economics and Management, Harbin Engineering University, Harbin 150001, ChinaSchool of Economics and Management, Harbin Engineering University, Harbin 150001, China; Department of Physics, University of Science, Pyongyang 950003, Democratic People's Republic of Korea; Corresponding author.School of Economics and Management, Harbin Engineering University, Harbin 150001, China; Department of Material Engineering, Kimchaek University of Technology, Pyongyang 950003, Democratic People's Republic of KoreaSchool of Economics and Management, Harbin Engineering University, Harbin 150001, ChinaAgriculture and Life Science Department, Pyongyang University of Science and Technology, Pyongyang 950003, Democratic People's Republic of KoreaCluster analysis is widely used in fields such as economics, management and engineering. The distance and correlation are two of the most important and often used mathematics- and statistics-based similarity measures in cluster analysis. Many studies have been conducted to improve the distance and similarity in high-dimensional and overlapped data. However, these studies do not consider the degree of influence (weight) of different properties on different types of data. In practice, the weight of each property is different, so these methods cannot accurately analyze real data. First, this study proposes a new distance measure that can reflect the weight, so that non-spherical overlapping data in the Euclidean space can be projected onto a weighted Euclidean space to form non-overlapping data. Second, the Fuzzy-ANP method is used to determine the weight of each factor. Then, by applying the Fuzzy-ANP-Weighted-Distance-QC (FAWQC) method to weighted random data, the effectiveness of the method is verified. Finally, the method is applied to the 2015 Economics-Energy-Environment (3E) data for 19 provinces in China for a comparative study of the classification of the system structure and evaluation of the low-carbon economy development level. The experiment results show that the FAWQC method can more accurately analyze real-world data than other methods.http://www.sciencedirect.com/science/article/pii/S240584401833072XEconomics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fan Decheng Song Jon Cholho Pang Wang Dong CholJin Won |
spellingShingle |
Fan Decheng Song Jon Cholho Pang Wang Dong CholJin Won Improved quantum clustering analysis based on the weighted distance and its application Heliyon Economics |
author_facet |
Fan Decheng Song Jon Cholho Pang Wang Dong CholJin Won |
author_sort |
Fan Decheng |
title |
Improved quantum clustering analysis based on the weighted distance and its application |
title_short |
Improved quantum clustering analysis based on the weighted distance and its application |
title_full |
Improved quantum clustering analysis based on the weighted distance and its application |
title_fullStr |
Improved quantum clustering analysis based on the weighted distance and its application |
title_full_unstemmed |
Improved quantum clustering analysis based on the weighted distance and its application |
title_sort |
improved quantum clustering analysis based on the weighted distance and its application |
publisher |
Elsevier |
series |
Heliyon |
issn |
2405-8440 |
publishDate |
2018-11-01 |
description |
Cluster analysis is widely used in fields such as economics, management and engineering. The distance and correlation are two of the most important and often used mathematics- and statistics-based similarity measures in cluster analysis. Many studies have been conducted to improve the distance and similarity in high-dimensional and overlapped data. However, these studies do not consider the degree of influence (weight) of different properties on different types of data. In practice, the weight of each property is different, so these methods cannot accurately analyze real data. First, this study proposes a new distance measure that can reflect the weight, so that non-spherical overlapping data in the Euclidean space can be projected onto a weighted Euclidean space to form non-overlapping data. Second, the Fuzzy-ANP method is used to determine the weight of each factor. Then, by applying the Fuzzy-ANP-Weighted-Distance-QC (FAWQC) method to weighted random data, the effectiveness of the method is verified. Finally, the method is applied to the 2015 Economics-Energy-Environment (3E) data for 19 provinces in China for a comparative study of the classification of the system structure and evaluation of the low-carbon economy development level. The experiment results show that the FAWQC method can more accurately analyze real-world data than other methods. |
topic |
Economics |
url |
http://www.sciencedirect.com/science/article/pii/S240584401833072X |
work_keys_str_mv |
AT fandecheng improvedquantumclusteringanalysisbasedontheweighteddistanceanditsapplication AT songjon improvedquantumclusteringanalysisbasedontheweighteddistanceanditsapplication AT cholhopang improvedquantumclusteringanalysisbasedontheweighteddistanceanditsapplication AT wangdong improvedquantumclusteringanalysisbasedontheweighteddistanceanditsapplication AT choljinwon improvedquantumclusteringanalysisbasedontheweighteddistanceanditsapplication |
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1724504841522249728 |