| Popis: |
Missing or inconsistent data has been a pervasive problem in data analysis since the origin of data collection. The management of missing data in organizations has recently been addressed as more firms implement large-scale enterprise resource planning systems (see Vosburg & Kumar, 2001; Xu et al., 2002). The issue of missing data becomes an even more pervasive dilemma in the knowledge discovery process, in that as more data is collected, the higher the likelihood of missing data becomes. The objective of this research is to discuss imprecise data and the data mining process. The article begins with a background analysis, including a brief review of both seminal and current literature. The main thrust of the chapter focuses on reasons for data inconsistency along with definitions of various types of missing data. Future trends followed by concluding remarks complete the chapter. |
