Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a generic dynamic programming method to compute the optimal clustering of $n$ scalar elements into $k$ pairwise disjoint intervals. This case includes 1D Euclidean $k$-means, $k$-medoids, $k$-medians, $k$-centers, etc.  extend the method to incorporate cluster size constraints and show how to choose the appropriate $k$ by model selection. Finally,  illustrate and refine the method on two case studies: Bregman clustering and statistical mixture learning maximizing the complete likelihood.
['Frank Nielsen', 'Richard Nock']
I am writing a research paper on a idea Optimal interval clustering: Application to Bregman clustering and statistical mixture learning
[{"from": "human", "value": "I am writing a research paper on a idea Optimal interval clustering: Application to Bregman clustering and statistical mixture learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a generic dynamic programming method to compute the optimal clustering of $n$ scalar elements into $k$ pairwise disjoint intervals. This case includes 1D Euclidean $k$-means, $k$-medoids, $k$-medians, $k$-centers, etc.  extend the method to incorporate cluster size constraints and show how to choose the appropriate $k$ by model selection. Finally,  illustrate and refine the method on two case studies: Bregman clustering and statistical mixture learning maximizing the complete likelihood."}]