Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss a generalization of the fuzzy (ighted) k-means clustering procedure and point out its relationships with data aggre- gation in spaces equipped with arbitrary dissimilarity measures. In the proposed setting, a data set partitioning is performed based on the notion of points' proximity to generic distance-based penalty minimizers. More- over, a new data classification algorithm, resembling the k-nearest neigh- bors scheme but less computationally and memory demanding, is intro- duced. Rich examples in complex data domains indicate the usability of the methods and aggregation theory in general.
['Anna Cena', 'Marek Gagolewski']
I am writing a research paper on a idea Fuzzy K-Minpen Clustering and K-nearest-minpen Classification Procedures Incorporating Generic Distance-Based Penalty Minimizers
[{"from": "human", "value": "I am writing a research paper on a idea Fuzzy K-Minpen Clustering and K-nearest-minpen Classification Procedures Incorporating Generic Distance-Based Penalty Minimizers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss a generalization of the fuzzy (ighted) k-means clustering procedure and point out its relationships with data aggre- gation in spaces equipped with arbitrary dissimilarity measures. In the proposed setting, a data set partitioning is performed based on the notion of points' proximity to generic distance-based penalty minimizers. More- over, a new data classification algorithm, resembling the k-nearest neigh- bors scheme but less computationally and memory demanding, is intro- duced. Rich examples in complex data domains indicate the usability of the methods and aggregation theory in general."}]