  Along with the flourish of the information age, massive amounts of data are
generated day by day. Due to the large-scale and high-dimensional
characteristics of these data, it is often difficult to achieve better
decision-making in practical applications. Therefore, an efficient big data
analytics method is urgently needed. For feature engineering, feature selection
seems to be an important research content in which is anticipated to select
"excellent" features from candidate ones. Different functions can be realized
through feature selection, such as dimensionality reduction, model effect
improvement, and model performance improvement. In many classification tasks,
researchers found that data seem to be usually close to each other if they are
from the same class; thus, local compactness is of great importance for the
evaluation of a feature. In this manuscript, we propose a fast unsupervised
feature selection method, named as, Compactness Score (CSUFS), to select
desired features. To demonstrate the efficiency and accuracy, several data sets
are chosen with extensive experiments being performed. Later, the effectiveness
and superiority of our method are revealed through addressing clustering tasks.
Here, the performance is indicated by several well-known evaluation metrics,
while the efficiency is reflected by the corresponding running time. As
revealed by the simulation results, our proposed algorithm seems to be more
accurate and efficient compared with existing algorithms.
