Membership Constraints in Formal Concept Analysis

Authors: Sebastian Rudolph, Christian Sacarea, Diana Troanca

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We analyze the computational complexity of this problem in general and for restricted forms of membership constraints. We perform the same analysis for generalizations of FCA to incidence structures of arity three (objects, attributes and conditions) and higher. We present a generic answer set programming (ASP) encoding of the membership constraint satisfaction problem, which allows for deploying available highly optimized ASP tools for its solution.
Researcher Affiliation Academia Sebastian Rudolph and Christian S ac area and Diana Troanc a Technische Universit at Dresden, Germany Universitatea Babes Bolyai, Romania sebastian.rudolph@tu-dresden.de {csacarea,dianat}@cs.ubbcluj.ro
Pseudocode Yes Algorithm 1 interactive n-concept finding algorithm function FINDNCONCEPTINTERACTIVE(K) ... Algorithm 2 propagation of user decisions function PROPAGATE(K, C) ...
Open Source Code No As an obvious and immediate avenue for future work, we will implement and evaluate our navigation framework based on the ASP-based satisfiability checker described.
Open Datasets No The paper is theoretical, focusing on computational complexity and algorithm design. It uses illustrative examples (e.g., Figure 1, Figure 2) but does not involve empirical experiments on specific datasets.
Dataset Splits No The paper focuses on theoretical analysis and algorithm descriptions, not empirical validation. There are no mentions of training, validation, or test splits for datasets.
Hardware Specification No The paper is theoretical and does not describe any experimental setup that would require hardware specifications.
Software Dependencies No The paper discusses Answer Set Programming (ASP) and mentions that "highly optimized ASP tools can be used for its solution" but does not specify any particular software with version numbers as a dependency for its own work.
Experiment Setup No The paper is theoretical and does not describe any empirical experiments or their setup, thus no hyperparameters or system-level training settings are provided.