基于数据间的相似性为原则，以数据差异性为主，划分处理数据信息。多维去重

Based on the principle of similarity between the data, the data is divided and processed based on the difference of the data. When designing the model of the multi-dimensional de-duplication clustering algorithm, once there are multiple clustering methods and data information in the data, all data can be divided into different types, and focus on how to find a unique clustering method, and in actual use , To get the final data set by removing duplicates. Multi-dimensional analysis is the top priority of clustering. Before multi-dimensional analysis, it is necessary to solve the problem of fixed dimensionality.

Based on the similarity between data, the data difference is the main principle, and the data information is divided and processed. When designing the model, once there are many clustering methods and data information in the data memory, all data can be divided into different types, and it focuses on how to find a unique clustering method, and in practice, to get the final data set. Multidimensional analysis is the most important part of clustering division. Before multidimensional analysis, the fixed dimension problem should be solved<br>

Based on the principle of similarity between data, data difference is the main factor, and data information is divided and processed. When designing the model, the multi-dimensional de-duplication clustering algorithm can divide all data into different types once there are multiple clustering methods and data information in the data, and focus on how to find a unique clustering method. In actual use, the final data set can be obtained by de-duplication. Multidimensional analysis is the top priority of clustering division. Before multidimensional analysis, it is necessary to solve the problem of fixed dimensions