Decorrelating Adversarial Nets for Clustering Mobile Network Data
This addresses the challenge of adapting clustering algorithms for mobile network automation, though it is incremental as it builds on existing deep clustering methods.
The paper tackles the problem of applying deep clustering to mobile network data by proposing DANCE, a method that separates clustering-relevant from irrelevant features, and shows it outperforms state-of-the-art algorithms by a significant margin on a mobile network dataset.
Deep learning will play a crucial role in enabling cognitive automation for the mobile networks of the future. Deep clustering, a subset of deep learning, could be a valuable tool for many network automation use-cases. Unfortunately, most state-of-the-art clustering algorithms target image datasets, which makes them hard to apply to mobile network data due to their highly tuned nature and related assumptions about the data. In this paper, we propose a new algorithm, DANCE (Decorrelating Adversarial Nets for Clustering-friendly Encoding), intended to be a reliable deep clustering method which also performs well when applied to network automation use-cases. DANCE uses a reconstructive clustering approach, separating clustering-relevant from clustering-irrelevant features in a latent representation. This separation removes unnecessary information from the clustering, increasing consistency and peak performance. We comprehensively evaluate DANCE and other select state-of-the-art deep clustering algorithms, and show that DANCE outperforms these algorithms by a significant margin on a mobile network dataset.