A Framework for Clustering Uncertain Data Streams

Abstract-- In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely dete...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Charu C. Aggarwal, Philip S. Yu
• Keywords: Database, ICDE 2008, Synthetic Data Sets, Uncertain Data Management, Uncertain Data Streams