The MD-BK-means Construction Method for Library Reader Portraits

Abstract

Due to the rapid development of internet technology, knowledge acquisition has become more convenient and efficient in network operations. University libraries serve as important resources for readers to acquire knowledge, and online resources and services in libraries have become the main direction for readers to acquire knowledge at present. Research the use of binary K-means clustering algorithm and library reader portrait technology to optimize the design of the reader portrait module and construct a multidimensional and multi perspective reader feature system. Reuse Spark programming language and support vector machine to perform computational processing on reader profile data to ensure accurate segmentation of the dataset. Finally, three datasets were used to test the accuracy and efficiency of the algorithm. The experimental comparison shows that the mining and precision segmentation of parallel SVM on the dataset are 93.20%, 85.16%, and 79.35% on the sample set, respectively, in order to optimize the mining performance of the data. The MD multi view binary K-means algorithm has a total Mahalanobis distance of 3.543, 5.268, and 22.385 on the sample dataset, respectively, to demonstrate its superiority in clustering performance. Therefore, the multi view binary K-means algorithm based on Mahalanobis distance has high advantages in reader portrait technology design, and provides technical support and theoretical reference for library reader portrait technology.