# On interestingness measures of formal concepts

arXiv: https://arxiv.org/abs/1611.02646 · HTML: https://mlsanity.com/paper/1611.02646/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2016.

## Built on by (most cited first, 9 of 9)

- [Conceptual Views on Tree Ensemble Classifiers](https://mlsanity.com/paper/2302.05270/) (2023)
- [Exploring Scale-Measures of Data Sets](https://mlsanity.com/paper/2102.02576/) (2021)
- [Ordinal Motifs in Lattices](https://mlsanity.com/paper/2304.04827/) (2023)
- [Decision Concept Lattice vs. Decision Trees and Random Forests](https://mlsanity.com/paper/2106.00387/) (2021)
- [On Interpretability and Similarity in Concept-Based Machine Learning](https://mlsanity.com/paper/2102.12723/) (2021)
- [Detecting Important Patterns Using Conceptual Relevance Interestingness Measure](https://mlsanity.com/paper/2110.11262/) (2021)
- [Delta-Closure Structure for Studying Data Distribution](https://mlsanity.com/paper/2210.06926/) (2022)
- [Enhancing Actionable Formal Concept Identification with Base-Equivalent Conceptual-Relevance](https://mlsanity.com/paper/2312.14421/) (2023)
- [Boosting the Learning for Ranking Patterns](https://mlsanity.com/paper/2610.16511/) (2026)

## Abstract (arXiv)

Formal concepts and closed itemsets proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing domain taxonomies and ontologies. Exponential explosion makes it difficult to consider the whole concept lattice arising from data, one needs to select most useful and interesting concepts. In this paper interestingness measures of concepts are considered and compared with respect to various aspects, such as efficiency of computation and applicability to noisy data and performing ranking correlation.