关键词: 高维, 不确定, Clique算法, K近邻

Abstract: How to reduce the impact of uncertain data on high dimensional data clustering is the difficulty of current research. Aiming at the problem of low clustering accuracy caused by uncertain data and curse of dimensionality, the method of determining the uncertain data and then clustering the certain data was adopted. In the process of determining the uncertain data, uncertain data were divided into value uncertain data and dimension uncertain data, and were processed separately to improve algorithm efficiency. K-Nearest Neighbor (KNN) query combined with expected distance was used to obtain the approximate value of uncertain data with the least impact on the clustering results, so as to improve the clustering accuracy. After determining the uncertain data, the method of subspace clustering was adopted to avoid the impact of the curse of dimensionality. The experimental results show that high-dimensional uncertain data clustering algorithm based on Clique for Uncertain data (UClique) has good performance on UCI datasets, has good anti-noise performance and scalability, can obtain better clustering results on high dimensional data, and can achieve the experimental results with higher accuracy on different uncertain datasets, showing that the algorithm is robust and can effectively cluster high dimensional uncertain data.

Key words: high-dimension, uncertain, Clique (Clique for all data) algorithm, K-Nearest Neighbor (KNN)