Abstract
Due to the increasing prosperity of human life science and technology, many huge research results have been obtained, and the scientific research of molecular biology is developing rapidly. Therefore, the output of biological genome data has increased exponentially, which constitutes a huge amount of data analysis. The seemingly chaotic and massive amount of data information actually contains a large amount of data and information of great key scientific significance and value. Therefore, this kind of genomic data information not only contains the information content that describes the characteristics of human life but also contains the information content that can express the essence of the biological organism. It includes macroeconomic information that can reflect the basic structure and capabilities of living organisms and microinformation in related fields of molecular biology. This massive amount of genetic data is usually closely related to each other, can influence each other, and does not exist alone. In the article, the causes of uncertain data and the classification of uncertain data are introduced, and the basic concepts and related algorithms of data mining are explained. Focusing on the research and analysis of abnormal point detection and clustering algorithms in uncertain data mining technology, this paper solves the problem of how to obtain more diverse and accurate outlier detection and cluster analysis results in uncertain data. The results showed that whether it was related to obesity or not, the Lp(a) level of the sarcopenia group was significantly higher than that of the nonsarcopenia group. At the same time, the correlation analysis showed that ASM/height was negatively correlated with Lp(a). ASM/height is one of the criteria for diagnosing sarcoidosis, and it is also the core of the analysis. Among the 1956 tumor patients collected in this study, 432 had sarcopenia, accounting for 22.08%, and the incidence of sarcopenia in patients with gastrointestinal tumors increased.
Highlights
With the continuous advancement of informatization, the data possessed by various industries has shown explosive growth
This article explains the meaning of uncertain outlier detection through examples and analyzes the insufficiency of the distance-based uncertain outlier detection algorithm, that is, it ignores the distribution of neighbors around the data object
This paper proposes IDDOD, an outlier detection algorithm for uncertain data based on distance and density
Summary
With the continuous advancement of informatization, the data possessed by various industries has shown explosive growth These data are often massive, complex, different forms, and even new data structures. Data is likely to contain a lot of valuable information They will have a certain guiding effect on many fields including scientific research, business, medicine, and politics, and they even have a subversive influence. In this context, the emergence of data mining technology provides another effective solution for the analysis and processing of these data [2]
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