Most algorithms for the high-dimensional data clustering are not intuitive and the clustering results are difficult to explain. To solve these problems, a new method based on the interactive visualization technology was proposed in this paper. First, the entropy-weight was adopted to determine the main attributes and how to arrange them. Every data was described in an improved radar chart in which polar radius stood by attribute values and polar angles stood by the attribute weights. Then the points in the radar chart were clustered through applying an improved k-means algorithm. The number of clusters was not given before. And initial centers were optimized according to the point density and their distance. Finally, the experiment showed that the improved radar chart reflected the distribution of the data better and that the improved k-means algorithm was more efficient and accuracy.
Through the analysis of the structure and properties of camel wool, this paper has a more thorough understanding of camel wool and discusses the structure is how to influence on the properties, from the length, strength, fineness, crimp, fulling, moisture absorption, etc. There is very important significance on the application performance and product quality.
Modern Distance Education is a new type of teaching style which generated with the development of computer network technology and multimedia technology. Distance Education always aims at individualized education and personalized teaching-learning service. But how to discover the valuable discipline from a large amount of learners information and achieve personalized distance education is an issue for educational researchers to resolve urgently. This thesis discusses how to apply the decision tree method of data mining technology in distance education system to classify learners in order to achieve individualized teaching for different learners.
Transverse compression stress-strain curves of a single fiber having a diameter about 10 μm must be measured to obtain transverse compression mechanical properties of high performance fibers. RJY-1 thermo-mechanical analysis instrument that the smallest division value is 0.1 μm can measure the curves of the fibers by installing some auxiliary device on the instrument. The conclu- sion obtained from the features of the curve is that the Kevlar fiber showed a yielding in transverse compression, while Carbon, Ceramic and Glass fibers did not appear the yielding, and their com- pression curves were almost straight up to the point of brittleness. Transverse compression modulus, yield and breaking stress of Kevlar, Carbon and Glass fiber can be obtained from the curves.
The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on improved BP neural network. The improved BP neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on improved BP neural network are very precise and efficient.
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