Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution
Clustering with Manifold Learning and Incremental Technique
Abstract
Most deep clustering methods despite providing complex networks to learn
better from data, use a shallow clustering method. These methods have
difficulty in finding good clusters due to the lack of ability to handle
between local search and global search to prevent premature convergence.
In other words, they do not consider different aspects of the search and
it causes them to get stuck in the local optimum. In addition, the
majority of existing deep clustering approaches perform clustering with
the knowledge of the number of clusters, which is not practical in most
real scenarios where such information is not available. To address these
problems, this paper presents a novel automatic deep sparse clustering
approach based on an evolutionary algorithm called Multi-Trial
Vectorbased Differential Evolution (MTDE). Sparse auto-encoder is first
applied to extract embedded features. Manifold learning is then adopted
to obtain representation and extract the spatial structure of features.
Afterward, MTDE clustering is performed without prior information on the
number of clusters to find the optimal clustering solution. The proposed
approach was evaluated on various datasets, including images and
time-series. The results demonstrate that the proposed method improved
MTDE by 18.94% on average and compared to the most recent deep
clustering algorithms, is consistently among the top three in the
majority of datasets. Source code is available on Github:
https://github.com/parhamhadikhani/ADSMTDE_Clustering.