OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Density-based clustering of uncertain data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Hierarchical Density-Based Clustering of Uncertain Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficient Clustering of Uncertain Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Finding frequent items in probabilistic data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Quality-Aware Probing of Uncertain Data with Resource Constraints
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Cleaning uncertain data with quality guarantees
Proceedings of the VLDB Endowment
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
A Framework for Clustering Uncertain Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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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 available. 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 usually 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 explored. 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 improves after the selective exact value acquisition is applied.