Optimization on clustering method of the liquid drop fingerprint

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.