M
Mathias Kirsten
Researcher at Center for Information Technology
Publications - 5
Citations - 209
Mathias Kirsten is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Cluster analysis & Canopy clustering algorithm. The author has an hindex of 4, co-authored 5 publications receiving 205 citations.
Papers
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Book ChapterDOI
Relational Distance-Based Clustering
Mathias Kirsten,Stefan Wrobel +1 more
TL;DR: RDBC is a bottom-up agglomerative clustering algorithm for first-order representations that relies on distance information only and features a novel parameter-free pruning measure for selecting the final clustering from the cluster tree.
Book ChapterDOI
Distance based approaches to relational learning and clustering
TL;DR: This chapter describes in detail one relational distance measure that has proven very successful in applications, and introduces three systems that actually carry out relational distance-based learning and clustering: RIBL2, RDBC and FORC.
Journal Article
Extending K-means clustering to first-order representations
Mathias Kirsten,Stefan Wrobel +1 more
TL;DR: In this paper, two approaches of extending k-means clustering to work on first-order representations are evaluated: k-medoids and k-prototypes, and they are empirically evaluated on a standard benchmark problem with respect to clustering quality and convergence.
Book ChapterDOI
Extending K-Means Clustering to First-Order Representations
Mathias Kirsten,Stefan Wrobel +1 more
TL;DR: An in-depth evaluation of two approaches of extending k-means clustering to work on first-order representations shows that in this case indeed the k-medoids approach is a viable and fast alternative to existing agglomerative or top-down clustering approaches even for a small-scale dataset, while k-prototypes exhibited a number of deficiencies.