A new method for noise data detection based on DBSCAN and SVDD
Shengxuan Hao, Xiaofeng Zhou, Hong Song · 2015
To improve the quality of real datasets by remove noise data, a new method for noise data detection based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and support vector data description (SVDD) was proposed in this article. Firstly, classical DBSCAN algorithm was used to cluster the data and remove the outliers. Secondly, SVDD was used to train the grouped data according to the cluster result, and gained discriminant model for each group. All these discriminant models were used in whole dataset to classify the data. The point does not belong to any class is identified as noise data and be removed. Experimental studies are done using UCI dataset. It is shown that the method we proposed is considerably efficient.