Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the clustering of incomplete data, the processing of missing attribute values and the optimization procedure of clustering are always of concern. , a novel clustering method is proposed to cope with incomplete data. Owing to the uncertainty of missing values,  first estimate these values in the form of intervals using the nearest neighbor method, which utilizes information about the distribution of data and transforms incomplete data set into an interval-valued one. Then, a kernel method is introduced to increase the separability beten data by implicitly mapping them into a higher dimensional feature space, in which a kernel-induced distance is used to replace the Euclidean distance so that the data can be processed in the original data space.  realize the kernel clustering of incomplete data set by means of a gradient-based alternating optimization of interval data clustering based on the interval kernel distance. Finally, the experimental results demonstrate that the proposed approach is superior in terms of its clustering performance.
['Tianhao Li', 'Liyong Zhang', 'Wei Lu', 'Hui Hou', 'Xiaodong Liu', 'Witold Pedrycz', 'Chongquan Zhong']
I am writing a research paper on a idea Interval kernel Fuzzy C-Means clustering of incomplete data
[{"from": "human", "value": "I am writing a research paper on a idea Interval kernel Fuzzy C-Means clustering of incomplete data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the clustering of incomplete data, the processing of missing attribute values and the optimization procedure of clustering are always of concern. , a novel clustering method is proposed to cope with incomplete data. Owing to the uncertainty of missing values,  first estimate these values in the form of intervals using the nearest neighbor method, which utilizes information about the distribution of data and transforms incomplete data set into an interval-valued one. Then, a kernel method is introduced to increase the separability beten data by implicitly mapping them into a higher dimensional feature space, in which a kernel-induced distance is used to replace the Euclidean distance so that the data can be processed in the original data space.  realize the kernel clustering of incomplete data set by means of a gradient-based alternating optimization of interval data clustering based on the interval kernel distance. Finally, the experimental results demonstrate that the proposed approach is superior in terms of its clustering performance."}]