STATE: A Clustering Algorithm Focusing on Edges Instead of Centers

Abstract

With the expansion of data scale and the increase in data complexity, it is particularly important to accurately identify clusters and efficiently save clustering results. To address this, we propose a novel clustering algorithm, Shape clustering based on data field (STATE), which can quickly identify clusters of arbitrary shapes and greatly reduce the storage space of clustering results in any datasets without reducing the accuracy. STATE mainly focuses on finding the edges of clusters and directions of edges instead of clustering centers through the data field. The results of STATE are presented as the edges of clusters without data objects inside clusters and without noise. Extensive experiments show that STATE can recognize complex data distribution in noisy environments without discrimination and greatly save the storage space of clustering results. When it is applied in a real-world scene, facial feature extraction, STATE can recognize eyes, nose, mouth, eyebrows and facial contours automatically without calibrating key features or training. Using the extracted facial features, we achieve facial recognition with high accuracy.