卡耐基.梅隆大學的牛發寫的關于孤立點和數據清洗的文章，全英文，2003年完成，Probabilistic Noise Identification and Data Cleaning，Real world data is never as perfect as we would like it to be and can often suffer from corruptions that may impact interpretations of the data, models created from the data, and decisions made based on the data. One approach to this problem is to identify and remove records that contain corruptions. Unfortunately, if only certain fields in a record have been corrupted then usable, uncorrupted data will be lost. In this paper we present LENS, an approach for identifying corrupted fields and using the remaining noncorrupted fields for subsequent modeling and analysis.

點擊標簽查看更多相關資源：