Introduction: Errors in data collection and failure to pay attention to data that is noisy in the collection process for any reason cause problems in data-based analysis and, as a result, wrong decision-making. Therefore, solving the problem of missing or noisy data before processing and analysis is of vital importance in analytical systems. The purpose of this paper is to provide a method to identify noisy data, outliers, and missing data and provide a suitable solution for these data.
Methods: This study is applied research. Data mining techniques including binning smoothing and regression models have been used to identify and replace outlier and noisy data.
Results: The results of the tests performed in the real environment related to the data of social networks show the proper performance of the proposed method. It has also been shown that the proposed method has higher accuracy compared to the methods of binning smoothing, average and linear regression. So that for the data related to the tweet section, the mean squared error obtained for the proposed method was equal to 0.04, the binning smoothing method was equal to 0.38, the linear regression method was equal to 0.05 and the average method was equal to 0.06.
Conclusion: The method presented in this article can initially identify outlier data through one-third and two-thirds normal, and then replace the outlier data with a linear regression model, which results in improving the performance of using and processing information and improving human-information interaction
