Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

HE Li’ao (何力骜); WANG Qianqian (王茜蒨); ZHAO Yu (赵宇); LIU Li (刘莉); PENG Zhong (彭中)

doi:10.1088/1009-0630/18/6/11

Plasma Science and Technology > 2016 > 18(6): 647-653. > DOI: 10.1088/1009-0630/18/6/11

HE Li’ao (何力骜), WANG Qianqian (王茜蒨), ZHAO Yu (赵宇), LIU Li (刘莉), PENG Zhong (彭中). Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy[J]. Plasma Science and Technology, 2016, 18(6): 647-653. DOI: 10.1088/1009-0630/18/6/11

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Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

School of Optic Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: supported by Beijing Natural Science Foundation of China (No. 4132063)

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Received Date: November 07, 2015

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Abstract

Abstract

Supervised learning methods (eg. PLS-DA, SVM, etc.) have been widely used with laser-induced breakdown spectroscopy (LIBS) to classify materials; however, it may induce a low correct classification rate if a test sample type is not included in the training dataset. Unsupervised cluster analysis methods (hierarchical clustering analysis, K-means clustering analysis, and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper. The results of hierarchical clustering analysis using four different similarity measuring methods (single linkage, complete linkage, unweighted pair-group average, and weighted pair-group average) are compared. In K-means clustering analysis, four kinds of choosing initial centers methods are applied in our case and their results are compared. The classification results of hierarchical clustering analysis, K-means clustering analysis, and ISODATA are analyzed. The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS.
- unsupervised learning methods,
- cluster analysis,
- laser-induced breakdown spectroscopy (LIBS)

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References

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