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

TL;DR

KnAC enhances clustering results by integrating expert knowledge and explanations, allowing for refinement and validation of clusters, and is compatible with any clustering algorithm, demonstrated through artificial and real-world examples.

Contribution

Introduces KnAC, a model-agnostic approach that incorporates background knowledge and explanations to improve clustering analysis.

Findings

KnAC outperforms classic clustering algorithms in experiments.

The method is applicable to any clustering algorithm.

Demonstrated effectiveness on both artificial and real-world data.

Abstract

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.