DBGSA: A Novel Data Adaptive Bregman Clustering Algorithm
Ying Xiao Hou-biao Li Yu-pu Zhang
With the development of Big data technology, data analysis has become
increasingly important. Traditional clustering algorithms such as K-means are
highly sensitive to the initial centroid selection and perform poorly on
non-convex datasets. In this paper, we address these problems by proposing a
data-driven Bregman divergence parameter optimization clustering algorithm
(DBGSA), which combines the Universal Gravitational Algorithm to bring similar
points closer in the dataset. We construct a gravitational coefficient equation
with a special property that gradually reduces the influence factor as the
iteration progresses. Furthermore, we introduce the Bregman divergence
generalized power mean information loss minimization to identify cluster
centers and build a hyperparameter identification optimization model, which
effectively solves the problems of manual adjustment and uncertainty in the
improved dataset. Extensive experiments are conducted on four simulated
datasets and six real datasets. The results demonstrate that DBGSA
significantly improves the accuracy of various clustering algorithms by an
average of 63.8\% compared to other similar approaches like enhanced clustering
algorithms and improved datasets. Additionally, a three-dimensional grid search
was established to compare the effects of different parameter values within
threshold conditions, and it was discovered the parameter set provided by our
model is optimal. This finding provides strong evidence of the high accuracy
and robustness of the algorithm.