7 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
Sibylle Hess, Wouter Duivesteijn
Most convex and nonconvex clustering algorithms come with one crucial parameter: the in -means. To this day, there is not one generally accepted way to accurately determine…
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization
Sibylle Hess, Nico Piatkowski, Katharina Morik
Boolean matrix factorization (BMF) is a popular and powerful technique for inferring knowledge from data. The mining result is the Boolean product of two matrices, approximating th…
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering
Sibylle Hess, Wouter Duivesteijn, Philipp Honysz +1
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporatin…
C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization
Sibylle Hess, Katharina Morik
Given labeled data represented by a binary matrix, we consider the task to derive a Boolean matrix factorization which identifies commonalities and specifications among the classes…
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization
Sibylle Hess, Katharina Morik, Nico Piatkowski
Mining and exploring databases should provide users with knowledge and new insights. Tiles of data strive to unveil true underlying structure and distinguish valuable information f…