  This paper introduces a novel K-means clustering algorithm, an advancement on
the conventional Big-means methodology. The proposed method efficiently
integrates parallel processing, stochastic sampling, and competitive
optimization to create a scalable variant designed for big data applications.
It addresses scalability and computation time challenges typically faced with
traditional techniques. The algorithm adjusts sample sizes dynamically for each
worker during execution, optimizing performance. Data from these sample sizes
are continually analyzed, facilitating the identification of the most efficient
configuration. By incorporating a competitive element among workers using
different sample sizes, efficiency within the Big-means algorithm is further
stimulated. In essence, the algorithm balances computational time and
clustering quality by employing a stochastic, competitive sampling strategy in
a parallel computing setting.
