17 citations · 40 across the 13 of their papers we have counts for
16 papers
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…
Differentially Private Tree-Based Redescription Mining
Matej Mihelčić, Pauli Miettinen
Differential privacy provides a strong form of privacy and allows preserving most of the original characteristics of the dataset. Utilizing these benefits requires one to design sp…
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…
Boolean matrix factorization meets consecutive ones property
Nikolaj Tatti, Pauli Miettinen
Boolean matrix factorization is a natural and a popular technique for summarizing binary matrices. In this paper, we study a problem of Boolean matrix factorization where we additi…
Hybrid ASP-based Approach to Pattern Mining
Sergey Paramonov, Daria Stepanova, Pauli Miettinen
Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all…
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…
Reductions for Frequency-Based Data Mining Problems
Stefan Neumann, Pauli Miettinen
Studying the computational complexity of problems is one of the - if not the - fundamental questions in computer science. Yet, surprisingly little is known about the computational…
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…
What You Will Gain By Rounding: Theory and Algorithms for Rounding Rank
Stefan Neumann, Rainer Gemulla, Pauli Miettinen
When factorizing binary matrices, we often have to make a choice between using expensive combinatorial methods that retain the discrete nature of the data and using continuous meth…
Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques
Saskia Metzler, Stephan Günnemann, Pauli Miettinen
Cliques are frequently used to model communities: a community is a set of nodes where each pair is equally likely to be connected. But studying real-world communities reveals that…
On Defining SPARQL with Boolean Tensor Algebra
Saskia Metzler, Pauli Miettinen
The Resource Description Framework (RDF) represents information as subject-predicate-object triples. These triples are commonly interpreted as a directed labelled graph. We propose…
Clustering Boolean Tensors
Saskia Metzler, Pauli Miettinen
Tensor factorizations are computationally hard problems, and in particular, are often significantly harder than their matrix counterparts. In case of Boolean tensor factorizations…
Scalable Boolean Tensor Factorizations using Random Walks
Dóra Erdős, Pauli Miettinen
Tensors are becoming increasingly common in data mining, and consequently, tensor factorizations are becoming more and more important tools for data miners. When the data is binary…