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Poisson Subspace Clustering: Focusing on the Essentials in Count Data
Collin Leiber, Kai Puolamäki, Heikki Mannila
Count data represented as a matrix of non-negative integer values, such as contingency tables, are prevalent across diverse domains. When clustering such data sets, specific method…
Khatri-Rao Clustering for Data Summarization
Martino Ciaperoni, Collin Leiber, Aristides Gionis +1
As datasets continue to grow in size and complexity, finding succinct yet accurate data summaries poses a key challenge. Centroid-based clustering, a widely adopted approach to add…
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
Collin Leiber, Lukas Miklautz, Claudia Plant +1
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about…
Breaking the Reclustering Barrier in Centroid-based Deep Clustering
Lukas Miklautz, Timo Klein, Kevin Sidak +5
This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners c…
Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters
Collin Leiber, Niklas Strauß, Matthias Schubert +1
Finding meaningful groups, i.e., clusters, in high-dimensional data such as images or texts without labeled data at hand is an important challenge in data mining. In recent years,…
SHADE: Deep Density-based Clustering
Anna Beer, Pascal Weber, Lukas Miklautz +4
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analys…