# KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

arXiv: https://arxiv.org/abs/2112.08759 · HTML: https://mlsanity.com/paper/2112.08759/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 67 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Learning to cluster in order to transfer across domains and tasks](https://mlsanity.com/paper/1711.10125/) (2017)
- [Embedding Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/1806.01445/) (2018)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Explainable AI for Trees: From Local Explanations to Global Understanding](https://mlsanity.com/paper/1905.04610/) (2019)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [ExKMC: Expanding Explainable k-Means Clustering](https://mlsanity.com/paper/2006.02399/) (2020)
- [Visualizing Data using GTSNE](https://mlsanity.com/paper/2108.01301/) (2021)
- [An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization](https://mlsanity.com/paper/2004.02121/) (2020)
- [EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis](https://mlsanity.com/paper/2003.01670/) (2020)
- [Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases](https://mlsanity.com/paper/1910.05030/) (2019)
- [Learning Conceptual Space Representations of Interrelated Concepts](https://mlsanity.com/paper/1805.01276/) (2018)
- [COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints](https://mlsanity.com/paper/1803.11060/) (2018)
- [Computationally Efficient Feature Significance and Importance for Machine Learning Models](https://mlsanity.com/paper/1905.09849/) (2019)
- [Geodesic Learning via Unsupervised Decision Forests](https://mlsanity.com/paper/1907.02844/) (2019)

## Built on by (most cited first, 2 of 2)

- [Explainable Predictive Maintenance](https://mlsanity.com/paper/2306.05120/) (2023)
- [Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations](https://mlsanity.com/paper/2310.14894/) (2023)

## Abstract (arXiv)

Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum of clustering algorithms that can be used for this purpose. However, their practical applications share a common post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be the bottleneck in the process, especially in cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters but also conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KnAC). Its main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution is not restricted to any existing clustering algorithm. Instead, KnAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and a model-agnostic improvement of any state-of-the-art clustering method. We demonstrate the feasibility of our method on artificially, reproducible examples and in a real life use case scenario. In both cases, we achieved better results than classic clustering algorithms without augmentation.