2 code implementations • 14 Jul 2023 • Thibault Marette, Pauli Miettinen, Stefan Neumann
In this paper, we study the problem of visualizing \emph{a given clustering} of overlapping clusters in bipartite graphs and the related problem of visualizing Boolean Matrix Factorizations.
no code implementations • 3 Jun 2022 • Matej Mihelčić, Pauli Miettinen
Scientists performing research in the fields of biology, medicine and pharmacy often prefer NMF over other dimensionality reduction approaches (such as PCA) because the non-negativity of the approach naturally fits the characteristics of the domain problem and its result is easier to analyze and understand.
no code implementations • 5 Dec 2020 • Stefan Neumann, Pauli Miettinen
We provide an algorithm that, after one pass over the stream, recovers the set of clusters on the right side of the graph using sublinear space; to the best of our knowledge, this is the first algorithm with this property.
no code implementations • 5 Dec 2020 • Pauli Miettinen, Stefan Neumann
The goal of Boolean Matrix Factorization (BMF) is to approximate a given binary matrix as the product of two low-rank binary factor matrices, where the product of the factor matrices is computed under the Boolean algebra.
no code implementations • 17 Jan 2019 • Nikolaj Tatti, Pauli Miettinen
In this paper, we study a problem of Boolean matrix factorization where we additionally require that the factor matrices have consecutive ones property (OBMF).
no code implementations • 22 Aug 2018 • Sergey Paramonov, Daria Stepanova, Pauli Miettinen
We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP.
no code implementations • 18 Jan 2018 • Sanjar Karaev, James Hook, Pauli Miettinen
We present an algorithm for our novel matrix factorization.
no code implementations • 19 Jul 2017 • Sanjar Karaev, Pauli Miettinen
In this paper we concentrate on the use of matrix factorizations for finding patterns from the data.