Abstract
Due to the huge potential in gene expression analysis, which is helpful for disease diagnosis, new drug development, and life science research, the two-way clustering algorithm was proposed and it was widely used in gene expression data research. In order to understand the economic data of medical and health industry, this paper analyzes the economic data of the medical and health industry in different regions of China based on blockchain technology and two-way spectral cluster analysis and makes statistics on the economic data of the medical and health industry in eastern, central, and western regions of China. This paper studies the development status of China’s medical and health industry and the factors affecting the agglomeration of medical and health service industry and analyzes them under the blockchain technology and two-way spectral cluster analysis method. The results show that the overall development trend of China’s medicine and health is from government-led to government, society, and individual sharing. After the transformation of blockchain technology and two-way spectral cluster analysis, the output value of the pharmaceutical industry increased by about 10%.
Highlights
Traditional clustering analysis algorithms mainly deal with static data information
Different from the general clustering method, in twoway clustering, the genes must be clustered, and the changes in experimental conditions must be considered at the same time. e clustering composed of object subsets and attribute subsets identifies gene combinations with consistent expression patterns in the subsets of specific conditions, that is, two-way clustering. e clustering changes found in the dynamic analysis of data flow play an important role in the economic data fusion analysis of medical and health industry
Beyca integrates multiple in situ sensor signals to detect initial abnormalities in the ultraprecision machining (UPM) process. rough the development of a new supervised learning method, the DP model state estimation is combined with the evidence theory sensor data fusion method to make a cohesive decision about the UPM process conditions
Summary
Traditional clustering analysis algorithms mainly deal with static data information. Due to the high-speed, real-time, and continuous characteristics of real-time data streams, traditional clustering analysis algorithms cannot be used. Rough the development of a new supervised learning method, the DP model state estimation is combined with the evidence theory sensor data fusion method to make a cohesive decision about the UPM process conditions. It is detected and classified as 90% accuracy [7]. E comparison results show that the blockchain-based technology and the two-way spectral clustering analysis method proposed in this paper are implemented in indicators, and time performance is better than other methods It compares and analyzes the experimental results of the algorithm in this paper and other commonly used biclustering integration methods in expressing data. e comparison results show that the blockchain-based technology and the two-way spectral clustering analysis method proposed in this paper are implemented in indicators, and time performance is better than other methods
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