A recognition method of celestial spectra outliers based on concept lattice is proposed by regarding the intension of the concept lattice nodes as characteristic subspace of the celestial spectra.

The data dimension reduction is the main method that can enhance the outliers mining efficiency based on higher-dimension data set.

General clustering approaches,such as distance-based and statistics-based,are not adapted to classification of outliers because of their characteristic of fewness data and sparseness.

In the article described the stray data mining elementary theory as well as the classical algorithm,and according to the characteristics of data in the past dozens years,they made a theory that the use of outlier data mining models to predict diseases and insect pests.

An improved density-based outlier mining algorithm;

Algorithm of finding Outlier for reclustering based on distance;

the paper proposed a TCP-based group-transmission scheme suitable for multi-subnet and multilayer networks.

The authors have analyzed statistical parameters including path-length,clustering coefficient and degree distribution of public transport networks in 5 Chinese cities.

The study is assisted by matlab software in order to get topology and some static state geometry quantities such as degree distribution,clustering coefficient,average path length and so on.

This paper,from the quantitative perspective,firstly puts up with the basic procedure of the establishment of social network model in CI study and secondly,probes into how to use three fundamental index:distribution dense,clustering coefficient,network diameter analyzes the social network.