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- W4285816265 abstract "With the development of the information industry, people are faced with an increasing amount of data. In order to obtain the intrinsic relationship and implicit information from these large-scale data, data mining as an important method has been paid more and more attention by people, and cluster analysis is an important research direction of data mining. In the era of big data, massive multi-modal data exists widely. How to mine the huge hidden value of data through complementary learning between modal data is the main problem of big data research at this stage, and it is also the task of big data and traditional data learning. Mixed attribute datasets are the most common type of datasets in the real world, especially in commercial financial databases, but there are very few clustering algorithms suitable for such datasets. The method used in this paper is machine learning, which enables computers to simulate human learning behavior, automatically acquire knowledge and skills through learning, continuously improve performance, and achieve self-improvement." @default.
- W4285816265 created "2022-07-19" @default.
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- W4285816265 date "2022-04-14" @default.
- W4285816265 modified "2023-09-29" @default.
- W4285816265 title "Research on fast extraction algorithm of big data information based on machine learning" @default.
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- W4285816265 doi "https://doi.org/10.1145/3544109.3544398" @default.
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