Biblio

Fast search of hyperspectral data is crucial in many practical applications ranging from classification to finding duplicate fragments in images. In this paper, we evaluate two space partitioning data structures in the task of searching hyperspectral data. In particular, we consider vp-trees and ball-trees, study several tree construction algorithms, and compare these structures with the brute force approach. In addition, we evaluate vp-trees and ball-trees with four similarity measures, namely, Euclidean Distance, Spectral Angle Mapper Bhattacharyya Angle, and Hellinger distance.

K-anonymity is a popular model used in microdata publishing to protect individual privacy. This paper introduces the idea of ball tree and projection area density partition into k-anonymity algorithm.The traditional kd-tree implements the division by forming a super-rectangular, but the super-rectangular has the area angle, so it cannot guarantee that the records on the corner are most similar to the records in this area. In this paper, the super-sphere formed by the ball-tree is used to address this problem. We adopt projection area density partition to increase the density of the resulting recorded points. We implement our algorithm with the Gotrack dataset and the Adult dataset in UCI. The experimentation shows that the k-anonymity algorithm based on ball-tree and projection area density partition, obtains more anonymous groups, and the generalization rate is lower. The smaller the K is, the more obvious the result advantage is. The result indicates that our algorithm can make data usability even higher.