My research covers data science from method development to applications. The most important fields I have developed methods for are matrix and tensor decompositions over non-standard algebras, redescription mining, and analysis of social networks. Their applications range from data mining, ecological niche modelling, the analysis of parliamentary candidates' opinions, and the analysis of voluntary-based network services to health data analysis.
For more information, please see my home page (link at the end of the page).
Faculty of Science and Forestry, School of Computing
Boolean matrix factorization meets consecutive ones property. Tatti, Nikolaj; Miettinen, Pauli / Proceedings of the 2019 SIAM International Conference on Data Mining. 2019. 2019
Metzler, Saskia; Günnemann, Stephan; Miettinen, Pauli. 2019. Stability and dynamics of communities on online question-answer sites Social networks 58: 50-58. 2019
Metzler, Saskia; Miettinen, Pauli. 2019. HyGen: generating random graphs with hyperbolic communities Applied network science 4: 53. 2019
Karaev, Sanjar; Miettinen, Pauli. 2018. Algorithms for Approximate Subtropical Matrix Factorization Data mining and knowledge discovery 2019; 33 2: 526-576. 2018
Random Graph Generators for Hyperbolic Community Structures. Metzler, Saskia; Miettinen, Pauli / Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018 (volume 1). 2018. 2018
I am responsible for coordinating the data science education at School of Computing. Personally I teach Introduction to Algorithmic Data Analysis and Short Introduction to Algorithmic Data Analysis at bachelor's level (in Finnish), and Matrix Decomposition Methods in Data Analysis and Graph Mining courses at master's level (in English).
Research groups and research projects
I am the head for the Algorithmic Data Analysis research group. I am a member of the North Savo Regional SOTE AI Hub project.