Open AccessMultivalued function recognition based on spectral clustering
Author(s) -
Zongchao Huang,
Zhaogong Zhang,
Guanwen Yu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1453/1/012145
Subject(s) - computer science , data mining , cluster analysis , big data , identifiability , function (biology) , data set , spectral clustering , process (computing) , algorithm , artificial intelligence , machine learning , evolutionary biology , biology , operating system
In scientific research, bioinformatics, Internet applications, e-commerce and many other application fields, the amount of data is growing at an extremely fast rate. To analyze and utilize these huge data resources, it is necessary to rely on effective data analysis technology. Big data application is a process of mining effective information from big data by using data analysis methods, providing auxiliary decisions for users and realizing the value of big data. In the process of data calculation and analysis, we will often find that the data from the same source will show multiple function images in the same coordinate system, which will make the same variable correspond to multiple values in data prediction analysis. We call it multivalued function here. This paper provides a method to identify multivalued functions. By using the maximum information coefficient (MIC) theory proposed by David n. Reshef, and using the data sampling method to calculate the function identifiability of the data set, then, the spectral clustering method is used for recognition, segment and mark the images of different functions. Finally, the regression function equations with different marks are obtained by Gauss-newton iteration method. The results can be used for data prediction and analysis to assist decision-makers to make reasonable judgments.