Local outliers detection is an important issue in data mining. By analyzing the limitations of the existing outlier detection algorthms, a local outlier detection algorthm based on coefficient of variation is introduced. This algorthms applies K-means which is strong in outliers searching, divides data set into sections, puts outliers and their nearing clusters into a local neighbourhood, then figures out the local deviation factor of each local neighbourhood by coefficient of variation, as a result, local outliers can more likely be found.The heoretic analysis and experimental results indicate that the method is ef fective and efficient.
RODA: A Fast Outlier Detection Algorithm Supporting Multi-Queries