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- W2350206509 abstract "The existing outlier detection algorithms should be improved due to their versatility,effectiveness,user-friendliness,and the performance in processing high-dimensional and large databases.This paper proposed a fast and effective hierarchical clustering based global outlier detection approch.Agglomerative hierarchical clustering was performed firstly,and then the isolated degree of the data could be visually judged and the number of the outliers could be determined based on the clustering tree and the distance matrix.After that,the outliers was identified unsupervisedly from the top to down of the clustering tree.Experimental results show that,this approach can identify global outliers fastly and effectively,and is user-friendly and capable at datasets of various shapes.Experiments also illustrate that this approach is suitable for using on high-dimensional and large databases." @default.
- W2350206509 created "2016-06-24" @default.
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- W2350206509 date "2011-01-01" @default.
- W2350206509 modified "2023-09-27" @default.
- W2350206509 title "Global outlier detection based on hierarchical clustering" @default.
- W2350206509 hasPublicationYear "2011" @default.
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