Purpose. In client information, string outliers need to be detected and cleaned. At present, many outlier detection algorithms only focus on the semantics of data, and ignore the structure, so it is difficult to ensure the accuracy of outlier detection. In order to address this issue, outlier detection method based on similarity distance is suggested in this paper. Methodology. We formulated the similarity calculation model of string data by combining with semantic and structure factors. According to the outlier detection theory in data cleansing, one-dimensional string data were projected to two-dimensional space and string outlier data were detected by using a new similarity measurement mechanism in the two-dimensional space. Findings. We first got the word frequency of string data by using the matrix calculation. Then the semantic similarity and structure similarity were calculated by using word frequency. After the string data mapping from one-dimensional to two-dimensional space, we obtained the outlier data by using the similarity distance. Originality. We made a study of string outlier detection in data cleansing. Firstly, we formulated the similarity calculation model by considering the semantic factor and structure factor. Secondly, by constructing the similarity cell to project the string data, we fulfilled the similarity distance measurement in the similarity cell. Practical value. The method can be used to clean the outlier string data in client information for any enterprise so that to ensure the data quality of client information, and reduce the costs of data maintenance. Extensive simulation experiments have been conducted to prove the feasibility and rationality of this method. The results showed that this method allows improving the accuracy of string outlier detection.

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