The optimization arithmetic of K-means clustering based on Indirect Feature Weight Learning

The performance of K-means clustering algorithm depended on the selection of distance metrics, there was a problem with Dimension Trap. Using the feature learning parameter can solve this problem, but the choice of feature learning was difficult, so the improper choice of feature learning would affect the convergence speed of clustering algorithm, even leading to non-convergence. In regard to the choice of feature learning, a new clustering method is discussed. The method of feature learning Indirect Feature Weight Learning automatically is adopted to protect more rapid convergence and improve the clustering performance. The result in testing data in typical UCI machine learning repository indicate that these measures have improved clustering performance.