Explainable clustering with multidimensional bounding boxes

Kuk, Michał; Bobek, Szymon; Nalepa, Grzegorz J.

doi:10.1109/dsaa53316.2021.9564220

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In our approach, we aim mainly to provide a method that will address all of the above four issues. The starting point of this work was the preliminary results introduced at the IEEE DSAA 2021 Conference [22]. Here, we present a fully developed approach, enclosed within a methodological framework for cluster analysis with multidimensional prototypes (CLAMP) and evaluated on a real-life industrial case and benchmark datasets.…”

Section: Clustering Algorithmmentioning

confidence: 99%

Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes

et al. 2022

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Explainable Artificial Intelligence (XAI) aims to introduce transparency and intelligibility into the decision-making process of AI systems. Most often, its application concentrates on supervised machine learning problems such as classification and regression. Nevertheless, in the case of unsupervised algorithms like clustering, XAI can also bring satisfactory results. In most cases, such application is based on the transformation of an unsupervised clustering task into a supervised one and providing generalised global explanations or local explanations based on cluster centroids. However, in many cases, the global explanations are too coarse, while the centroid-based local explanations lose information about cluster shape and distribution. In this paper, we present a novel approach called CLAMP (Cluster Analysis with Multidimensional Prototypes) that aids experts in cluster analysis with human-readable rulebased explanations. The developed state-of-the-art explanation mechanism is based on cluster prototypes represented by multidimensional bounding boxes. This allows representing of arbitrary shaped clusters and combines the strengths of local explanations with the generality of global ones. We demonstrate and evaluate the use of our approach in a real-life industrial case study from the domain of steel manufacturing as well as on the benchmark datasets. The explanations generated with CLAMP were more precise than either centroid-based or global ones.

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Section: Clustering Algorithmmentioning

confidence: 99%

Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes

et al. 2022

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“…XAI techniques can improve the communication with users by utilising graphs, images, and text to expose the reasoning behind what was executed, what the current state is, and what could happen next [13]. When unexpected situations develop, and where a schedule is updated, XAI is used to provide transparency [14] within a system to build trust and understanding for the generated results retrieved by the GSO. This is achieved by allowing investigation of queries, concerns, and/or impact to the mission objectives [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Abstract Argumentation for Explainable Satellite Scheduling

Powell

Riccardi

2022

2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)

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Satellite schedules are derived from satellite mission objectives, which are mostly managed manually from the ground. This increases the need to develop autonomous on-board scheduling capabilities and reduce the requirement for manual management of satellite schedules. Additionally, this allows the unlocking of more capabilities on-board for decision-making, leading to an optimal campaign. However, there remain trust issues in decisions made by Artificial Intelligence (AI) systems, especially in risk-averse environments, such as satellite operations. Thus, an explanation layer is required to assist operators in understanding decisions made, or planned, autonomously on-board. To this aim, a satellite scheduling problem is formulated, utilizing real world data, where the total number of actions are maximised based on the environmental constraints that limit observation and down-link capabilities. The formulated optimisation problem is solved with a Constraint Programming (CP) method. Later, the mathematical derivation for an Abstract Argumentation Framework (AAF) for the test case is provided. This is proposed as the solution to provide an explanation layer to the autonomous decision-making system. The effectiveness of the defined AAF layer is proven on the daily schedule of an Earth Observation (EO) mission, monitoring land surfaces, demonstrating greater capabilities and flexibility, for a human operator to inspect the machine provided solution.

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