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The PEAC project addresses the challenges of clustering data by developing and analyzing algorithms that improve the efficiency and accuracy of clustering methods. It aims to bridge the gap between theoretical performance and practical application in data science.
Clustering data according to similarity is ubiquitous in computer and data sciences.
Similarity between data is often modeled by a distance function: two data points are close if they are similar.
This induces a metric space in which each data point is associated to a point of the space.
Thus, a clustering according to similarity is a partition of the points such that the distance between two points in the same part is small.
Therefore, clustering problems play a crucial role in extracting infor…
KOBENHAVNS UNIVERSITET
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