Data Clustering with Quantum Mechanics
Data clustering is a vital tool for data analysis. This work shows that some existing useful methods in data clustering are actually based on quantum mechanics and can be assembled into a powerful and accurate data clustering method where the efficiency of computational quantum chemistry eigenvalue...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-01-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2227-7390/5/1/5 |
| _version_ | 1819058062639497216 |
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| author | Tony C. Scott Madhusudan Therani Xing M. Wang |
| author_facet | Tony C. Scott Madhusudan Therani Xing M. Wang |
| author_sort | Tony C. Scott |
| collection | DOAJ |
| description | Data clustering is a vital tool for data analysis. This work shows that some existing useful methods in data clustering are actually based on quantum mechanics and can be assembled into a powerful and accurate data clustering method where the efficiency of computational quantum chemistry eigenvalue methods is therefore applicable. These methods can be applied to scientific data, engineering data and even text. |
| first_indexed | 2024-12-21T13:49:14Z |
| format | Article |
| id | doaj.art-73d12a335b9644bdb9d14a2f1d243f24 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-12-21T13:49:14Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-73d12a335b9644bdb9d14a2f1d243f242022-12-21T19:01:46ZengMDPI AGMathematics2227-73902017-01-0151510.3390/math5010005math5010005Data Clustering with Quantum MechanicsTony C. Scott0Madhusudan Therani1Xing M. Wang2College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, ChinaNear India Pvt Ltd., No. 71/72, Jyoti Nivas College Road, Koramangala, Bengalore 560095, IndiaSherman Visual Lab, Sunnyvale, CA 94085, USAData clustering is a vital tool for data analysis. This work shows that some existing useful methods in data clustering are actually based on quantum mechanics and can be assembled into a powerful and accurate data clustering method where the efficiency of computational quantum chemistry eigenvalue methods is therefore applicable. These methods can be applied to scientific data, engineering data and even text.http://www.mdpi.com/2227-7390/5/1/5computational quantum mechanicsMeila–Shi algorithmquantum clusteringMATLAB |
| spellingShingle | Tony C. Scott Madhusudan Therani Xing M. Wang Data Clustering with Quantum Mechanics Mathematics computational quantum mechanics Meila–Shi algorithm quantum clustering MATLAB |
| title | Data Clustering with Quantum Mechanics |
| title_full | Data Clustering with Quantum Mechanics |
| title_fullStr | Data Clustering with Quantum Mechanics |
| title_full_unstemmed | Data Clustering with Quantum Mechanics |
| title_short | Data Clustering with Quantum Mechanics |
| title_sort | data clustering with quantum mechanics |
| topic | computational quantum mechanics Meila–Shi algorithm quantum clustering MATLAB |
| url | http://www.mdpi.com/2227-7390/5/1/5 |
| work_keys_str_mv | AT tonycscott dataclusteringwithquantummechanics AT madhusudantherani dataclusteringwithquantummechanics AT xingmwang dataclusteringwithquantummechanics |