Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: One of the major problems of K-means is that one must use dense vectors for its centroids, and therefore it is infeasible to store such huge vectors in memory when the feature space is high-dimensional.  address this issue by using feature hashing (inberger et al., 2009), a dimension-reduction technique, which can reduce the size of dense vectors while retaining sparsity of sparse vectors. Our analysis gives theoretical motivation and justification for applying feature hashing to K-means, by showing how much will the objective of K-means be (additively) distorted. Furthermore, to empirically verify our method,  experimented on a document clustering task.
['Hajime Senuma']
I am writing a research paper on a idea K-means Clustering with Feature Hashing
[{"from": "human", "value": "I am writing a research paper on a idea K-means Clustering with Feature Hashing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: One of the major problems of K-means is that one must use dense vectors for its centroids, and therefore it is infeasible to store such huge vectors in memory when the feature space is high-dimensional.  address this issue by using feature hashing (inberger et al., 2009), a dimension-reduction technique, which can reduce the size of dense vectors while retaining sparsity of sparse vectors. Our analysis gives theoretical motivation and justification for applying feature hashing to K-means, by showing how much will the objective of K-means be (additively) distorted. Furthermore, to empirically verify our method,  experimented on a document clustering task."}]