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Mathematical Modeling of Data Similarity Recognition Based on Ordered Clustering Equations

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DOI: 10.25236/icmmct.2024.028


Yajing Pan

Corresponding Author

Yajing Pan


In the information age, data has permeated every aspect of our lives, forming a vast ocean of data. How to discover potential valuable information from these data is an important research direction in the field of data science. Especially in the network environment, the complexity of data is becoming increasingly prominent. The mixed existence of structured, semi-structured, and unstructured data makes data processing and analysis exceptionally complex. Meanwhile, the possible similarity between data blocks also increases the difficulty of identifying data similarity. To effectively address this challenge, this paper proposes a mathematical model for data similarity recognition based on ordered clustering equations. This model aims to discover the potential similarity between data through in-depth analysis and processing of data, and then conduct orderly clustering. Through this model, we can further improve the utilization of data, reduce data redundancy, and better mine the potential value of data. At the same time, this model can also provide strong support for other data science research and promote the development of data science.


Ordered clustering equation; Data similarity recognition; mathematical modeling