AugDMC: Data Augmentation Guided Deep Multiple Clustering
Jiawei Yao Enbei Liu Maham Rashid Juhua Hu
Clustering aims to group similar objects together while separating dissimilar
ones apart. Thereafter, structures hidden in data can be identified to help
understand data in an unsupervised manner. Traditional clustering methods such
as k-means provide only a single clustering for one data set. Deep clustering
methods such as auto-encoder based clustering methods have shown a better
performance, but still provide a single clustering. However, a given dataset
might have multiple clustering structures and each represents a unique
perspective of the data. Therefore, some multiple clustering methods have been
developed to discover multiple independent structures hidden in data. Although
deep multiple clustering methods provide better performance, how to efficiently
capture the alternative perspectives in data is still a problem. In this paper,
we propose AugDMC, a novel data Augmentation guided Deep Multiple Clustering
method, to tackle the challenge. Specifically, AugDMC leverages data
augmentations to automatically extract features related to a certain aspect of
the data using a self-supervised prototype-based representation learning, where
different aspects of the data can be preserved under different data
augmentations. Moreover, a stable optimization strategy is proposed to
alleviate the unstable problem from different augmentations. Thereafter,
multiple clusterings based on different aspects of the data can be obtained.
Experimental results on three real-world datasets compared with
state-of-the-art methods validate the effectiveness of the proposed method.