基于权重搜索树改进K近邻的高维分类算法

信息采集技术日益发展导致的高维、大规模数据,给数据挖掘带来了巨大挑战,针对K近邻分类算法在高维数据分类中存在效率低、时间成本高的问题,提出基于权重搜索树改进K近邻(K-nearest neighbor algorithm based on weight search tree,KNN-WST)的高维分类算法,该算法根据...

Improved k-nearest neighbor algorithm based on weight search tree for high-dimensional classification

The ongoing development of information acquisition technique resulted in high-dimensional and large-scale data, it enormously challenges the data mining. Aiming at low efficiency and high time cost of K-nearest neighbor classification algorithms in high-dimensional data, an Improved k-nearest neighbor algorithm based on weight search tree (WSTKnn) for high-dimensional classification was proposed in this paper. The algorithm selects some attributes as nodes to construct a search tree according to the weight of feature attributes. The search tree divides the data set into different matrix regions. Unknown samples need to find the search tree to obtain the most similar matrix region, and only calculate the distance from the data contained in the matrix area. thus, reduce data size to reduce time complexity. And discussed the Minkowski Distance that would be most suitable for distance measurement of high-dimensional data. Simulation experiments on 6 standard high-dimensional data show that the classification time of WSTKnn has better performance than K-nearest neighbor, Decision Tree and SVM, the classification time is significantly reduced and the classification accuracy is better than other algorithms. WSTKnn has better performance on the classification of high-dimensional data, which is expected to give some references for solving the related problem of high-dimensional data.