Abstract: The traditional K-means algorithm often leads to unstable clustering quality due to the randomness of the initial clustering center selection and tends to fall into suboptimal solutions when ...

Abstract: Computational effort is difficult when dealing with high dimensional data that has hundreds or thousands of features. Features that don't significantly influence class predictions throughout ...