Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Here,  propose a clustering technique for general clustering problems including those that have non-convex clusters. For a given desired number of clusters $K$,  use three stages to find a clustering. The first stage uses a hybrid clustering technique to produce a series of clusterings of various sizes (randomly selected). They key steps are to find a $K$-means clustering using $K_\ell$ clusters where $K_\ell \gg K$ and then joins these small clusters by using single linkage clustering. The second stage stabilizes the result of stage one by reclustering via the `membership matrix' under Hamming distance to generate a dendrogram. The third stage is to cut the dendrogram to get $K^*$ clusters where $K^* \geq K$ and then prune back to $K$ to give a final clustering. A variant on our technique also gives a reasonable estimate for $K_T$, the true number of clusters. #R##N# provide a series of arguments to justify the steps in the stages of our methods and  provide numerous examples involving real and simulated data to compare our technique with other related techniques.
['Saeid Amiri', 'Bertrand Clarke', 'Jennifer Clarke', 'Hoyt A. Koepke']
I am writing a research paper on a idea A General Hybrid Clustering Technique
[{"from": "human", "value": "I am writing a research paper on a idea A General Hybrid Clustering Technique"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Here,  propose a clustering technique for general clustering problems including those that have non-convex clusters. For a given desired number of clusters $K$,  use three stages to find a clustering. The first stage uses a hybrid clustering technique to produce a series of clusterings of various sizes (randomly selected). They key steps are to find a $K$-means clustering using $K_\\ell$ clusters where $K_\\ell \\gg K$ and then joins these small clusters by using single linkage clustering. The second stage stabilizes the result of stage one by reclustering via the `membership matrix' under Hamming distance to generate a dendrogram. The third stage is to cut the dendrogram to get $K^*$ clusters where $K^* \\geq K$ and then prune back to $K$ to give a final clustering. A variant on our technique also gives a reasonable estimate for $K_T$, the true number of clusters. #R##N# provide a series of arguments to justify the steps in the stages of our methods and  provide numerous examples involving real and simulated data to compare our technique with other related techniques."}]