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Sami Hanhijärvi

Researcher at University of Helsinki

Publications -  15
Citations -  1334

Sami Hanhijärvi is an academic researcher from University of Helsinki. The author has contributed to research in topics: Cluster analysis & Exploratory data analysis. The author has an hindex of 11, co-authored 15 publications receiving 1186 citations. Previous affiliations of Sami Hanhijärvi include Helsinki Institute for Information Technology & Helsinki University of Technology.

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Continental-scale temperature variability during the past two millennia

Moinuddin Ahmed, +86 more
- 21 Apr 2013 - 
TL;DR: The authors reconstructed past temperatures for seven continental-scale regions during the past one to two millennia and found that the most coherent feature in nearly all of the regional temperature reconstructions is a long-term cooling trend, which ended late in the nineteenth century.
Proceedings Article

Randomization Techniques for Graphs

TL;DR: This paper focuses on randomization techniques for unweighted undirected graphs for graph mining within the framework of statistical hypothesis testing, and describes three alternative algorithms based on local edge swapping and Metropolis sampling.
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.
Journal ArticleDOI

Pairwise comparisons to reconstruct mean temperature in the Arctic Atlantic Region over the last 2,000 years

TL;DR: PaiCo as mentioned in this paper uses the ordering of all pairs of proxy observations within each record to arrive at a consensus time series that best agrees with all proxy records, which is subsequently calibrated to the instrumental record to provide an estimate of past climate.