Abstract
We present methods for finding patterns using sorting algorithms. Two modifications of diffusion-invariant pattern clustering are proposed, which allow finding structurally similar objects under study. The proposed modifications allow you to reduce the time spent on searching for templates, which allows you to work with big datasets. It assumes endogenous quantification and distribution of income patterns. The methodology for adjusting the obtained results is described. The method for searching structurally close objects in the presence of errors in the initial data selection is suggested. The possibility of correcting the final results is very important given the high sensitivity of the methodology to the presence of errors in the initial dataset. The proposed algorithmic solutions are demonstrated using a practical example. The results are compared with varying methods of cluster analysis.
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Acknowledgment
The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’.
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Myachin, A. (2022). Finding Structurally Similar Objects Based on Data Sorting Methods. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-10461-9_56
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DOI: https://doi.org/10.1007/978-3-031-10461-9_56
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