Data Selection for Exact Value Acquisition to Improve Uncertain Clustering
In recent years, data uncertainty widely attracts researchers attention because the amount of imprecise data is growing rapidly. Although data are not known exactly, probability distributions or expected errors are sometimes avail able. While most researchers on uncertain data mining are looking for methods to extract mining results from uncertain data, which is usually in the form of probability distributions or expected errors, it is also very important to lower the data uncertainty by making a part of data more certain to help get better mining results. For example, input values of some sensors in the sensor network are usu ally designed to be recorded more frequently than others because they are more important or more likely to change. In this paper, the issue of selecting a part of uncertain data and acquiring their exact values to improve clustering results is ex plored. Under a general uncertainty model, we propose both global and localized data selection methods, which can be used together with any existing uncertain clustering algorithm. Experimental results show that the quality of clustering im proves after the selective exact value acquisition is applied.
Yu-Chieh Lin De-Nian Yang Ming-Syan Chen
Department of Electrical Engineering, National Taiwan University, Taiwan Institute of Information Science, Academia Sinica, Taiwan Department of Electrical Engineering, National Taiwan University, Taiwan Research Center for Informa
国际会议
11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)
九寨沟
英文
459-470
2010-07-14（万方平台首次上网日期，不代表论文的发表时间）