Data Mining Of Populus Germplasm Resources

This paper first discusses the significance of studying and protection on germplasm resources, and then expounds the basic theory and application of data mining studies in vast scopes.Based on the summarization, this paper selects 6 clustering methods such as k-means, k-mediods, FCM, CURE, SOM, GA and so on to lucubrate after comparing various clustering algorithms which are over partition, nesting, fuzzy, machine learning, artificial intelligence data mining methods. Meanwhile, this study implements the designed clustering software which is a multifunction-integrated and universal-used software system by using the object oriented programming language C#.This paper also researches into the cluster validity problem which is used to evaluate the validity of clustering. Two-dimension surface points' clustering testing proves that the actualized software is availably, the SD validity index is logical and scientific. From this research and related studies we found that: i. Kohonen's Self-Organizing Maps clustering method is more efficiently and more steadily, ii. The SD index is not only use for clustering-validity, but also for finding the optimal partitioning number of a data set. These are all worth to farther deeply study.At last, this study executes the data mining process, carries out the clustering-analysis on the leaf-factor data set of 150 Populus clones, and extracts several typical "group knowledge". In conclusion, this research acquires a series of significative result, reach it's anticipate achievements.