An optimal and stable algorithm for clustering numerical data
In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be obtained for every run. In real-world applications,...
| Published in: | Algorithms |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109399419&doi=10.3390%2fa14070197&partnerID=40&md5=28e9e298aa7e11e0021cdaf07826ebad |