Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a novel mode seeking and clustering procedure based on the estimation of the discrete probability density function of the data set. The discrete density is estimated in two steps. Initial density estimate is acquired by counting data samples which populate each cell of the discretized domain. Based on the initial density estimate, each cell of the discretized domain is assigned a variable bandwidth kernel, which is afterwards used to compute final discrete density estimate. Modes of the estimated density, corresponding to the patterns in the input data, are obtained by hill climbing procedure. The proposed technique is highly efficient, running in time linear to the number of input data samples, with low constant factors. The proposed technique has no embedded assumptions about structure of the data or the feature space, like number of clusters or their shape, thus arbitrarily structured data sets can be analyzed.
['Damir Krstinić', 'Ivan Slapničar']
I am writing a research paper on a idea Grid-based mode seeking procedure
[{"from": "human", "value": "I am writing a research paper on a idea Grid-based mode seeking procedure"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a novel mode seeking and clustering procedure based on the estimation of the discrete probability density function of the data set. The discrete density is estimated in two steps. Initial density estimate is acquired by counting data samples which populate each cell of the discretized domain. Based on the initial density estimate, each cell of the discretized domain is assigned a variable bandwidth kernel, which is afterwards used to compute final discrete density estimate. Modes of the estimated density, corresponding to the patterns in the input data, are obtained by hill climbing procedure. The proposed technique is highly efficient, running in time linear to the number of input data samples, with low constant factors. The proposed technique has no embedded assumptions about structure of the data or the feature space, like number of clusters or their shape, thus arbitrarily structured data sets can be analyzed."}]