Compactness Score: A Fast Filter Method for Unsupervised Feature Selection

TL;DR

This paper introduces CSUFS, a fast unsupervised feature selection method based on local data compactness, demonstrating improved accuracy and efficiency in clustering tasks on various datasets.

Contribution

The paper proposes a novel, efficient unsupervised feature selection algorithm called CSUFS that leverages local compactness for better clustering performance.

Findings

CSUFS outperforms existing methods in accuracy.

CSUFS is faster in running time.

The method improves clustering quality on multiple datasets.

Abstract

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.