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Application of an K-means Improved Clustering Analysis Algorithm in the Design of Resource Management Information System

The K-Means algorithm is a typical algorithm in the partition-based clustering algorithm. The algorithm has the simple operation advantages, using the error squared criterion function, and high compressibility and scalability in the large data sets processing. However, the algorithm still has some shortcomings that the random initial clustering center leads to the instability of the algorithm. This article studies the ideas, principles, disadvantages and advantages of the traditional K-Means algorithm and proposes and studies the defects of its dependence on the initial value. An improved algorithm K-means improved, which improves the method of selecting initial cluster centers. Experiments have shown that the K-means improved algorithm effectively improves the algorithm stability and efficiency and reduces the algorithm cost.