Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In discrete k-center and k-median clustering,  are given a set of points P in a metric space M, and the task is to output a set C P, jCj = k, such that the cost of clustering P using C is as small as possible. For k-center, the cost is the furthest a point has to travel to its nearest center, whereas for k-median, the cost is the sum of all point to nearest center distances. In the fault-tolerant versions of these problems,  are given an additional parameter 1 ‘ k, such that when computing the cost of clustering, points are assigned to their ‘th nearest-neighbor in C, instead of their nearest neighbor.  provide constant factor approximation algorithms for these problems that are both conceptually simple and highly practical from an implementation stand-point.
['Nirman Kumar', 'Benjamin Raichel']
I am writing a research paper on a idea Fault Tolerant Clustering Revisited
[{"from": "human", "value": "I am writing a research paper on a idea Fault Tolerant Clustering Revisited"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In discrete k-center and k-median clustering,  are given a set of points P in a metric space M, and the task is to output a set C P, jCj = k, such that the cost of clustering P using C is as small as possible. For k-center, the cost is the furthest a point has to travel to its nearest center, whereas for k-median, the cost is the sum of all point to nearest center distances. In the fault-tolerant versions of these problems,  are given an additional parameter 1 \u2018 k, such that when computing the cost of clustering, points are assigned to their \u2018th nearest-neighbor in C, instead of their nearest neighbor.  provide constant factor approximation algorithms for these problems that are both conceptually simple and highly practical from an implementation stand-point."}]