We applied our method on a soybean QTL database curation, from which 2278 records were extracted from 228 papers with a precision rate of 96.9% and a recall rate of 83.3%, F value for the method is 89.6%.

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.