A New Unsupervised Clustering Method Based on Ontlier Information
一种基于离群点信息的新型无监督聚类方法

There are various applications of clustering analysis techniques in the field of image retrieval. For the lack of valuable prior knowledge in the image retrieval process, unsupervised clustering algorithms should be applied. This paper proposes a new unsupervised clustering method: clustering algorithms will automatically stop according to the outlier information. This method also complements the shortages of current clustering algorithms in outlier detection and using. To show its feasibility, the paper proposes several improvements on two classical clustering algorithms, CURE and ROCK. The empirical results show that by using new method, these two algorithms can stop automatically and also achieve better performance.