PROXIMUS: Software for Summarization of Very High Dimensional Discrete-Valued Datasets PROXIMUS is a software tool for error-bounded approximation of high-dimensional binary attributed datasets based on nonorthogonal decomposition of binary matrices. This tool can be used for analyzing data arising in a variety of domains ranging from commercial to scientific applications. Using a combination of innovative algorithms, novel data structures, and efficient implementation, PROXIMUS computes a concise representation for very large binary matrices, providing insights into common patterns in the rows and columns of the matrix. PROXIMUS has found application in many areas, including association rule mining, DNA microarray analysis, and business analytics. The original release of PROXIMUS is implemented in C and is freely available as open source below. It was also implemented in R within the CBA (Clustering for Business Analytics) by Christian Buchta and Michael Hahsler and in Java by Jaan Ubi.

References in zbMATH (referenced in 13 articles )

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  1. Sripratak, Piyashat; Punnen, Abraham P.; Stephen, Tamon: The bipartite Boolean quadric polytope (2022)
  2. Hess, Sibylle; Pio, Gianvito; Hochstenbach, Michiel; Ceci, Michelangelo: BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent (2021)
  3. Fomin, Fedor V.; Golovach, Petr A.; Panolan, Fahad: Parameterized low-rank binary matrix approximation (2020)
  4. Krivulin, N. K.; Romanova, E. Yu.: On the rank-one approximation of positive matrices using tropical optimization methods (2019)
  5. Dong, Bo; Lin, Matthew M.; Park, Haesun: Integer matrix approximation and data mining (2018)
  6. Fomin, Fedor V.; Golovach, Petr A.; Panolan, Fahad: Parameterized low-rank binary matrix approximation (2018)
  7. Karapetyan, Daniel; Punnen, Abraham P.; Parkes, Andrew J.: Markov chain methods for the bipartite Boolean quadratic programming problem (2017)
  8. Schneider, Johannes; Vlachos, Michail: Scalable density-based clustering with quality guarantees using random projections (2017)
  9. Punnen, Abraham P.; Sripratak, Piyashat; Karapetyan, Daniel: The bipartite unconstrained 0-1 quadratic programming problem: polynomially solvable cases (2015)
  10. Jiang, Peng; Peng, Jiming; Heath, Michael; Yang, Rui: A clustering approach to constrained binary matrix factorization (2014)
  11. van Leeuwen, Matthijs; Vreeken, Jilles; Siebes, Arno: Identifying the components (2009) ioport
  12. Chi, Jie; Koyuturk, Mehmet; Grama, Ananth: CONQUEST: A coarse-grained algorithm for constructing summaries of distributed discrete datasets (2006)
  13. Koyuturk, Mehmet; Grama, Ananth; Ramakrishnan, Naren: Compression, Clustering, and Pattern Discovery in Very High-Dimensional Discrete-Attribute Data Sets (2005) ioport