For a recommender system (RS), it is difficult to capture all the user’s interest lists simultaneously, which leads to the problem of insufficient performance of the existing joint RS based on the K-Means clustering algorithm. In this paper (1), we introduce a cluster optimization method OP -K-means for user preference data. This method starts with propagation from the center of the user preference data. By selecting relatively distant positions between each initial center, the distance between them is increased as much as possible. (2) Finally, we validate the effectiveness of our algorithm on a dataset from Facebook and compare our algorithm with original K-means. Our experimental results justify the validity of our OP -K-means algorithm.

In the last decade, oil-based titanium dioxide nanofluids (TiO2 NFs) have attracted great interests due to their unique insulating properties and excellent thermal performance making them potential applications in the...

With the highest efficiency of gathering spectra by LAMOST telescope, a large number of spectra have been obtained during commissioning observation, which included a lot of spectra of O type star. It's a difficult task to obtain accurate parameters for hot star, lacking of a good model. Several stellar models, such as MAFAGS, ATLAS, Marcs etc, do not cover the parameter range which temperature exceeds 25000K. POLLUX is a database of synthetic stellar spectra, in which CMFGEN provides atmosphere models for the O type stars (Teff >25000K) [5]. A method of estimating stellar parameters for hot stars is presented in this paper, based on matching LAMOST observed spectra with the theoretical spectra library. We convert the resolution of CMFGEN spectra, which is about 150000 to LAMOST resolution of 2000. By comparing with the CMFGEN template spectra, we can obtain the parameters of observed hot stars. Estimation for the errors of the final parameters shows that low efficiency of LAMOST blue arms of the spectrographs does not affect O type star observations.

In order to explore the intelligent algorithms that can be used in the modern design of rural green buildings, this paper conducts factor analysis based on the modern design requirements of rural green buildings, and constructs a data mining algorithm suitable for factor analysis of rural green buildings on the basis of quantitative analysis. Data mining technology uses energy consumption software to simulate and analyze the energy consumption of the building, calculate the energy consumption in the normal operation stage, and calculate the corresponding energy consumption and multiply it by the carbon emission factor of the corresponding energy to derive the carbon emissions during the operation phase. In addition, this paper combines the actual situation of rural green buildings to conduct a multifaceted architectural case analysis, and combines the actual situation to construct a system functional structure. Finally, this paper verifies the method proposed in this paper through experimental research. From the research results, it can be seen that the method proposed in this paper has a better effect.

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