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| Recently, mining uncertain data is gaining considerable attention due to more and more applications, such as sensor database, location database, biometric information systems, produce uncertain data. Though there exist a lot of approaches to cluster the uncertain data, none of them address mining and visualizing uncertain data. In this paper, we propose a new neural network algorithm called uncertain self-organizing map (USOM) which combines fuzzy distance function and self-organizing map to mine and visualize the uncertain data. The self-organizing map assigns the high dimensional data to the corresponding neurons and projects them on a low-dimensional grid which consists of the neurons. Each neuron is viewed as a small cluster which is a collection of the uncertain data. We merge the neurons in the low-dimensional grid to form the bigger clusters by minimal spanning tree. The experiments show that the new approaches works well in the uncertain dataset. |
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| Keywords:Computer science; self-organizing maps; uncertain data |
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