Uncertain Data Mining: A New Research Direction
Michael Chau, Reynold C. K. Cheng, Ben Kao · PolyU Institutional Research Archive (Hong Kong Polytechnic University) · 2005
Data uncertainty is often found in real-world applications due to reasons such as imprecise measurement, outdated sources, or sampling errors. Recently, much research has been published in the area of managing data uncertainty in databases. We propose that when data mining is performed on uncertain data, data uncertainty has to be considered in order to obtain high quality data mining results. We call this the Uncertain Data Mining problem. In this paper, we present a framework for possible research directions in this area. We also present the UK-means clustering algorithm as an example to illustrate how the traditional K-means algorithm can be modified to handle data uncertainty in data mining.