N
Niko Vuokko
Researcher at Helsinki University of Technology
Publications - 12
Citations - 307
Niko Vuokko is an academic researcher from Helsinki University of Technology. The author has contributed to research in topics: Sample (statistics) & Cluster analysis. The author has an hindex of 9, co-authored 12 publications receiving 299 citations. Previous affiliations of Niko Vuokko include Helsinki Institute for Information Technology & Aalto University.
Papers
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Proceedings ArticleDOI
Tell me something I don't know: randomization strategies for iterative data mining
TL;DR: The problem of randomizing data so that previously discovered patterns or models are taken into account, and the results indicate that in many cases, the results of, e.g., clustering actually imply theresults of, say, frequent pattern discovery.
Proceedings ArticleDOI
Tell Me Something I Don't Know: Randomization Strategies for Iterative Data Mining
TL;DR: In this paper, the problem of randomizing data so that previously discovered patterns or models are taken into account is considered, and the authors use Metropolis sampling based on local swaps to achieve this.
Proceedings Article
Randomization of real-valued matrices for assessing the significance of data mining results
TL;DR: Three alternative algorithms based on local transformations and Metropolis sampling are described, and it is shown that they are efficient and usable in practice and work efficiently and solve the defined problem.
Proceedings Article
Reconstructing Randomized Social Networks.
Niko Vuokko,Evimaria Terzi +1 more
TL;DR: This work identifies the cases in which the original network G and feature vectors F can be reconstructed in polynomial time and addresses the problem of reconstructing the originalnetwork and set of features given their randomized counterparts G and F and knowledge of the randomization model.
Journal IssueDOI
Randomization methods for assessing data analysis results on real-valued matrices
TL;DR: Methods based on local transformations and Metropolis sampling are described, and it is shown that the methods are efficient and usable in practice in significance testing of data mining results on real-valued matrices.