关键词: 孤立点检测, 层次聚类, 数据挖掘

Abstract: The existance of outlier always leads to inaccurate, even wrong results in data mining. The outlier detection algorithm now available should be improved including its versatility, effectiveness, user-friendliness, and the performance in processing high-dimensional and large databases. An effective and global outlier detection method is proposed. Agglomerative hierarchical clustering is performed, and the isolated degree of the data can be visually judged by the clustering tree and distance matrix, and the number of the outliers can be determined and the outliers are identified unsupervisedly from the top to down of the clustering tree. Experimental results show that the method can effectively detect the top-n global outliers, and applicable to datasets of various shapes. Experimental results show that the algorithm is efficient, user-friendly, and applicable to detect the outliers for high-dimensional and large databases.

Key words: outlier detection, hierarchical clustering, data mining