Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Existing hierarchical clustering algorithms involve a flat clustering component and an additional agglomerative or divisive procedure.  presents a density peak based hierarchical clustering method (DenPEHC), which directly generates clusters on each possible clustering layer, and introduces a grid granulation framework to enable DenPEHC to cluster large-scale and high-dimensional (LSHD) datasets. This study consists of three parts: (1) utilizing the distribution of the parameter  γ , which is defined as the product of the local density  ρ  and the minimal distance to data points with higher density  δ  in “clustering by fast search and find of density peaks” (DPClust), and a linear fitting approach to select clustering centers with the clustering hierarchy decided by finding the “stairs” in the  γ  curve; (2) analyzing the leading tree (in which each node except the root is led by its parent to join the same cluster) as an intermediate result of DPClust, and constructing the clustering hierarchy efficiently based on the tree; and (3) designing a framework to enable DenPEHC to cluster LSHD datasets when a large number of attributes can be grouped by their semantics. The proposed method builds the clustering hierarchy by simply disconnecting the center points from their parents with a linear computational complexity  O ( m ), where  m  is the number of clusters. Experiments on synthetic and real datasets show that the proposed method has promising efficiency, accuracy and robustness compared to state-of-the-art methods.
['J. Xu', 'Guoyin Wang', 'Weihui Deng']
I am writing a research paper on a idea DenPEHC: Density peak based efficient hierarchical clustering
[{"from": "human", "value": "I am writing a research paper on a idea DenPEHC: Density peak based efficient hierarchical clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Existing hierarchical clustering algorithms involve a flat clustering component and an additional agglomerative or divisive procedure.  presents a density peak based hierarchical clustering method (DenPEHC), which directly generates clusters on each possible clustering layer, and introduces a grid granulation framework to enable DenPEHC to cluster large-scale and high-dimensional (LSHD) datasets. This study consists of three parts: (1) utilizing the distribution of the parameter  \u03b3 , which is defined as the product of the local density  \u03c1  and the minimal distance to data points with higher density  \u03b4  in \u201cclustering by fast search and find of density peaks\u201d (DPClust), and a linear fitting approach to select clustering centers with the clustering hierarchy decided by finding the \u201cstairs\u201d in the  \u03b3  curve; (2) analyzing the leading tree (in which each node except the root is led by its parent to join the same cluster) as an intermediate result of DPClust, and constructing the clustering hierarchy efficiently based on the tree; and (3) designing a framework to enable DenPEHC to cluster LSHD datasets when a large number of attributes can be grouped by their semantics. The proposed method builds the clustering hierarchy by simply disconnecting the center points from their parents with a linear computational complexity  O ( m ), where  m  is the number of clusters. Experiments on synthetic and real datasets show that the proposed method has promising efficiency, accuracy and robustness compared to state-of-the-art methods."}]