Towards mobility-based clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Uncertain centroid based partitional clustering of uncertain data
Proceedings of the VLDB Endowment
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In recent years there has been a growing interest in clustering uncertain data. In contrast to traditional, "sharp" data representation models, uncertain data objects can be represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In this context, the focus has been mainly on partitional and density-based approaches, whereas hierarchical clustering schemes have drawn less attention.We propose a centroid-linkage-based agglomerative hierarchical algorithm for clustering uncertain objects, named U-AHC. The cluster merging criterion is based on an information-theoretic measure to compute the distance between cluster prototypes. These prototypes are represented as mixture densities that summarize the pdfs of all the uncertain objects in the clusters. Experiments have shown that our method outperforms state-of-the-art clustering algorithms from an accuracy viewpoint while achieving reasonably good efficiency.