Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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 beten 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.
['Francesco Gullo', 'Giovanni Ponti', 'Andrea Tagarelli', 'Sergio Greco']
I am writing a research paper on a idea A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach
[{"from": "human", "value": "I am writing a research paper on a idea A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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 beten 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."}]