pyDRESCALk is a software package for applying non-negative RESCAL decomposition in a distributed fashion to large datasets. It can be utilized for decomposing relational datasets. It can minimize the difference between reconstructed data and the original data through Frobenius norm. Additionally, the Custom Clustering algorithm allows for automated determination for the number of Latent features. pyDRESCALk is developed as part of the R&D 100 award wining SmartTensors project.


  author       = {Bhattarai, Manish and
                  Kharat, Namita and
                  Skau, Erik and
                  Truong, Duc and
                  Eren, Maksim and
                  Rajopadhye, Sanjay and
                  Djidjev, Hristo and
                  Alexandrov, Boian},
  title        = {pyDRESCALk: Python Distributed Non Negative RESCAL decomposition with determination of latent features},
  month        = dec,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.5758446},
  url          = {}

  title={Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization},
  author={Vangara, Raviteja and Bhattarai, Manish and Skau, Erik and Chennupati, Gopinath and Djidjev, Hristo and Tierney, Tom and Smith, James P and Stanev, Valentin G and Alexandrov, Boian S},
  journal={IEEE Access},

Maksim E. Eren
Maksim E. Eren

My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.