Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Normalized compression distance (NCD) is a parameter-free similarity measure based on compression. The NCD beten pairs of objects is not sufficient for al l applications.  propose an NCD of finite multisets (multiples) of objacts that is metric and is bette r for many applications. Previously, attempts to obtain such an NCD failed.  use the theoretical notion of Kolmogorov complexity that for practical purposes is approximated from above by the length of the compressed version of the file involved, using a real-world compression program.  applied the new NCD for multiples to retinal progenitor cell questions that re earlier treated with the pairwise NCD. Here  get significantly better results.  also applied the NCD for multiples to synthetic time sequence data. The preliminary results are as good as nearest neighbor Euclidean classifier. Index Terms— Normalized compression distance, multisets or multiples, pattern recognition, data mining, similarity, Kolmogorov complexity, retinal progenitor cell classification, synthetic data classification
['Andrew R. Cohen', 'Paul M. B. Vitányi']
I am writing a research paper on a idea Normalized Compression Distance of Multiples
[{"from": "human", "value": "I am writing a research paper on a idea Normalized Compression Distance of Multiples"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Normalized compression distance (NCD) is a parameter-free similarity measure based on compression. The NCD beten pairs of objects is not sufficient for al l applications.  propose an NCD of finite multisets (multiples) of objacts that is metric and is bette r for many applications. Previously, attempts to obtain such an NCD failed.  use the theoretical notion of Kolmogorov complexity that for practical purposes is approximated from above by the length of the compressed version of the file involved, using a real-world compression program.  applied the new NCD for multiples to retinal progenitor cell questions that re earlier treated with the pairwise NCD. Here  get significantly better results.  also applied the NCD for multiples to synthetic time sequence data. The preliminary results are as good as nearest neighbor Euclidean classifier. Index Terms\u2014 Normalized compression distance, multisets or multiples, pattern recognition, data mining, similarity, Kolmogorov complexity, retinal progenitor cell classification, synthetic data classification"}]