Abstract
In order to effectively reduce the time complexity of clustering algorithm, a new method based on multiple linear regression is put forward to reduce the eigenvector dimensions of the liquid drop fingerprint. After feature extraction with waveform analysis method applied on 38 kinds of liquid samples, optimization is carried out to decrease the 10 characteristic values to 8 values, which is then used in subsequent hierarchical clustering and dynamic clustering. Based on the first dynamic clustering results, comprehensive analysis is applied and dynamic clustering method is used once more. Experimental results show that the recognition ratio of the liquid drop fingerprint can be ensured, together with the reduced computational complexity and excellent clustering accuracy. Compared with hierarchical clustering method, the iterative dynamic clustering method is more effective in liquid identification, with its accuracy up to 100% among selected samples.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
