Abstract Detection and removal of outliers in a dataset is fundamental preprocessing task without which the analysis data can be misleading. Furthermore, existence anomalies heavily degrade performance machine learning algorithms. In order to detect an unsupervised manner, some novel statistical techniques are proposed this paper. The based on methods considering compactness other properties. n...

We propose in this paper a new measure called GOF (Group Outlier Factor) to detect groups outliers. To validate this measure we integrated it in a clustering process using Self-organizing Map. The proposed approach is based on relative density of each group of data and simultaneously provides a partitioning of data and a quantitative indicator (GOF). The obtained results are very encouraging to...

Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Detection of spatial outliers helps reveal valuable information from large spatial data sets. In many real applications, spatial objects can not be simply abstracted as isolated points. They have different boundary, size, volume, and location. These spatial properties affect the impact of a spatial...