17 citations · 40 across the 13 of their papers we have counts for
7 papers · 1 filter
Fast Redescription Mining Using Locality-Sensitive Hashing
Maiju Karjalainen, Esther Galbrun, Pauli Miettinen
Redescription mining is a data analysis technique that has found applications in diverse fields. The most used redescription mining approaches involve two phases: finding matching…
Visualizing Overlapping Biclusterings and Boolean Matrix Factorizations
Thibault Marette, Pauli Miettinen, Stefan Neumann
Finding (bi-)clusters in bipartite graphs is a popular data analysis approach. Analysts typically want to visualize the clusters, which is simple as long as the clusters are disjoi…
Finding Rule-Interpretable Non-Negative Data Representation
Matej Mihelčić, Pauli Miettinen
Non-negative Matrix Factorization (NMF) is an intensively used technique for obtaining parts-based, lower dimensional and non-negative representation. Researchers in biology, medic…
Biclustering and Boolean Matrix Factorization in Data Streams
Stefan Neumann, Pauli Miettinen
We study the clustering of bipartite graphs and Boolean matrix factorization in data streams. We consider a streaming setting in which the vertices from the left side of the graph…
Recent Developments in Boolean Matrix Factorization
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 matric…
Latitude: A Model for Mixed Linear-Tropical Matrix Factorization
Sanjar Karaev, James Hook, Pauli Miettinen
Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpr…
Algorithms for Approximate Subtropical Matrix Factorization
Sanjar Karaev, Pauli Miettinen
Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper…