Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via ights on the instance points, somehow making clustering move toward the hardest points to cluster. The motivations come principally from an analogy with porful supervised classification methods known as boosting algorithms. Hover, interest in this analogy has so far been mainly borne out from experimental studies only.  is, to the best of our knowledge, the first attempt at its formalization. More precisely,  handle clustering as a constrained minimization of a Bregman divergence. ight modifications rely on the local variations of the expected complete log-likelihoods. Theoretical results show benefits resembling those of boosting algorithms and bring modified (ighted) versions of clustering algorithms such as k-means, fuzzy c-means, expectation maximization (EM), and k-harmonic means. Experiments are provided for all these algorithms, with a readily available code. They display the advantages that subtle data reighting may bring to clustering
['Richard Nock', 'Frank Nielsen']
I am writing a research paper on a idea On weighting clustering
[{"from": "human", "value": "I am writing a research paper on a idea On weighting clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via ights on the instance points, somehow making clustering move toward the hardest points to cluster. The motivations come principally from an analogy with porful supervised classification methods known as boosting algorithms. Hover, interest in this analogy has so far been mainly borne out from experimental studies only.  is, to the best of our knowledge, the first attempt at its formalization. More precisely,  handle clustering as a constrained minimization of a Bregman divergence. ight modifications rely on the local variations of the expected complete log-likelihoods. Theoretical results show benefits resembling those of boosting algorithms and bring modified (ighted) versions of clustering algorithms such as k-means, fuzzy c-means, expectation maximization (EM), and k-harmonic means. Experiments are provided for all these algorithms, with a readily available code. They display the advantages that subtle data reighting may bring to clustering"}]