Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When data mining techniques are applied to uncertain data, their uncertainty has to be considered to obtain high quality results. Usually, an uncertain object is described by a probability density function, a probability density function is approximated by a large amount of sample points, and the distance beten two uncertain objects is expressed by the expected distance. Computing the expected distance is costly because it involves double integral using a large amount of sample points for two uncertain objects' probability density functions. This is critical for some uncertain data mining techniques. , a simple and efficient formula of evaluating the distance beten two uncertain objects is presented.  also give the application of the formula in nearest-neighbor classifying. Experiments with datasets based on UCI datasets and the plant dataset of “Three Parallel Rivers of Yunnan Protected Area” verify the formula is effective and efficient.
['Hongmei Chen', 'Lizhen Wang', 'Weiyi Liu', 'Qing Xiao']
I am writing a research paper on a idea An efficient method of evaluating the distance between two uncertain objects
[{"from": "human", "value": "I am writing a research paper on a idea An efficient method of evaluating the distance between two uncertain objects"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When data mining techniques are applied to uncertain data, their uncertainty has to be considered to obtain high quality results. Usually, an uncertain object is described by a probability density function, a probability density function is approximated by a large amount of sample points, and the distance beten two uncertain objects is expressed by the expected distance. Computing the expected distance is costly because it involves double integral using a large amount of sample points for two uncertain objects' probability density functions. This is critical for some uncertain data mining techniques. , a simple and efficient formula of evaluating the distance beten two uncertain objects is presented.  also give the application of the formula in nearest-neighbor classifying. Experiments with datasets based on UCI datasets and the plant dataset of \u201cThree Parallel Rivers of Yunnan Protected Area\u201d verify the formula is effective and efficient."}]