Blocked Time-Step Algorithm for Accelerating k-Means and Fuzzy c-Means

The k-means and fuzzy c-means algorithm are widely used for discovering clusters in data. In this paper, we propose a new acceleration method using blocked time step algorithm. It reduces the number of distance calculations efficiently by changing the frequency of updating membership grades (searching the nearest cluster centers). By restricted time step with the discrete number as a power of two, distance for the objective function will be calculated only synchronized objects at the blocked time. As a results, the experiments show the proposed algorithm reduces distance calculations by 20 -- 60% while keeping the clustering accuracy.