Density Peaks Clustering (DPC) algorithm is a new clustering algorithm with the advantages such as few adjustment parameters, no iterative solution and the capacity of finding non-spherical clusters. However, there are some disadvantages of the algorithm: the cutoff distance cannot be adjusted automatically, and the cluster centers need to be selected manually. For the above problems, an Adaptive DPC (ADPC) algorithm was proposed， the adjustment of adaptive cutoff distance based on Gini coefficient was realized, and an automatic acquisition strategy of clustering centers was established. Firstly, the calculation formula of cluster center weight was redefined by taking local density and relative distance into account at the same time. Then， the adjustment method of adaptive cutoff distance was established based on Gini coefficient. Finally, according to the decision graph and cluster center weight sort graph, the strategy of automatically selecting cluster centers was proposed. The simulation results show that, the ADPC algorithm can automatically adjust the cutoff distance and automatically acquire the clustering centers according to the characteristics of problem, and obtain better results than several commonly clustering algorithms and improved DPC algorithms on the test datasets.

To solve the Small Sample Size (SSS) problem and the "inferior" problem of traditional Fisher Linear Discriminant Analysis (FLDA) when it is applied to Synthetic Aperture Radar (SAR) image recognition tasks, a new image feature extraction technique was proposed based on weighted two-directional and two-dimensional linear discriminant analysis (W(2D)2LDA). First, the scatter matrices in the two-directional and two-dimensional linear discriminant analysis criterion were modified by adding weights. Then, feature matrix was extracted by W(2D)2LDA. The experimental results with MSTAR dataset verify the effectiveness of the proposed method, and it can strengthen the feature's discrimination and obtain better recognition performance with fewer memory requirements simultaneously.

When editing a word processing document, we may search the Web by using a term in the document as an initial query and then modifying the query by adding keywords extracted from the text surrounding the search term. There are query expansion methods which use the text surrounding the search term in the initial result to weight candidate keywords in the source document to modify the query. However, this approach may lead to topic drift. To solve the problem, the initial results were filtered first and only the results containing similar contexts as that in the source document were selected to help choosing additional keywords. Experiments show that this method can get more appropriate additional keywords than other methods.