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- W4312711241 abstract "Data mining is a concept of getting relevant data from massive databases. Nowadays, there is a lack of Prediction of disease is increased predominantly. Hence, we need to remove the inaccurate data that has been spread over in the dataset, which leads to inaccurately predicting disease. Outliers are more prone to prediction mechanisms. This paper includes the detection of outliers in disease prediction mechanisms. The goal of this paper is to bring out exact data instead of getting invalid data by finding outliers and handling them with care. Outliers can be detected using a KNN based peak-LOF based approach with manhattan distance metrics. In this paper, a proper study on outlier detection mechanisms has been done." @default.
- W4312711241 created "2023-01-05" @default.
- W4312711241 creator A5041947322 @default.
- W4312711241 creator A5088509495 @default.
- W4312711241 date "2022-07-15" @default.
- W4312711241 modified "2023-09-26" @default.
- W4312711241 title "KNN Based Peak-LOF for Outlier Detection" @default.
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- W4312711241 doi "https://doi.org/10.1109/icses55317.2022.9914212" @default.
- W4312711241 hasPublicationYear "2022" @default.
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