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Based on the five climatic factors such as monthly mean temperature, monthly mean maximum temperature, monthly average lowest temperature, the average monthly net radiation and the monthly mean precipitation in the breeding distribution area of the typical migratory species such as red-crowned crane in Northeast China from 1981 to 1990, this paper studied the characteristics analysis on the related climate data by using the theory and methods of interval statistics, hierarchical clustering based on normalization distance and clustering fusion technology. The results show that the most appropriate time in breeding habitat was May to September. The conclusion coincided with the biological characteristics and ecological characteristics of red-crowned crane in Northeast China. Extracting climatic factors from climate data was critical to the predictive distribution of species. The used climatic factors in the past mostly depended on the biological and ecological characteristics of species and the correlation analysis for climatic factors in the distribution area of species. Instead, this method only depended on climate data in the distribution area of the predictive species entirely, and it obtained data mining and data processing. This set of research methods possess generality and will provide basis for the research about the impact of future climate change on the predictive distribution of the migratory species in Northeast China. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the research on granular space theory and structural clusters of metric based on granular space is introduced, and a comprehensive of theoretical and analyzing methodologies is developed. The granular space theory is established based on normalized equicrural metric, and the consistent cluster characteristics of ordered granular spaces are derived. Details are shared for some related subject, such as the granular representation of structural cluster from normalized metric, the optimal cluster determination, the fusion of structural cluster, and the structural clustering analysis of metric space based on granular space, etc. Theories and methodologies are established for structural clustering based on metric space, and developed a series of mathematical models and formal description tools for the research on its potential applications. Direct and geometric interpretations for structural clustering analysis are provided to assist deeper understanding the essence of clustering (or classifying) procedure. [ABSTRACT FROM AUTHOR]