20 citations · 66 across the 10 of their papers we have counts for
11 papers
The Geometric Structure of Topic Models
Johannes Hirth, Tom Hanika
Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics.…
Automatic Textual Explanations of Concept Lattices
Johannes Hirth, Viktoria Horn, Gerd Stumme +1
Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such…
Ordinal Motifs in Lattices
Johannes Hirth, Viktoria Horn, Gerd Stumme +1
Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of…
Scaling Dimension
Bernhard Ganter, Tom Hanika, Johannes Hirth
Conceptual Scaling is a useful standard tool in Formal Concept Analysis and beyond. Its mathematical theory, as elaborated in the last chapter of the FCA monograph, still has room…
Conceptual Views on Tree Ensemble Classifiers
Tom Hanika, Johannes Hirth
Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance…
Conceptual Views of Neural Networks: A Framework for Neuro-Symbolic Analysis
Johannes Hirth, Tom Hanika
We introduce \emph{conceptual views} as a formal framework grounded in Formal Concept Analysis for globally explaining neural networks. Experiments on twenty-four ImageNet models a…
Research Topic Flows in Co-Authorship Networks
Bastian Schäfermeier, Johannes Hirth, Tom Hanika
In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertis…
Quantifying the Conceptual Error in Dimensionality Reduction
Tom Hanika, Johannes Hirth
Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning. They affect patterns in and dependencies on data dimensions and ult…
Exploring Scale-Measures of Data Sets
Tom Hanika, Johannes Hirth
Measurement is a fundamental building block of numerous scientific models and their creation. This is in particular true for data driven science. Due to the high complexity and siz…
On the Lattice of Conceptual Measurements
Tom Hanika, Johannes Hirth
We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, and derive a canonical represe…
Knowledge Cores in Large Formal Contexts
Tom Hanika, Johannes Hirth
Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essent…