Explainable clustering and application to wealth management compliance

Horel, Enguerrand; Giesecke, Kay; Storchan, Victor; Chittar, Naren

doi:10.1145/3383455.3422530

Cited by 11 publications

(14 citation statements)

References 7 publications

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“…The process which allows explaining clustering generally involves a three-step explanation procedure that changes an unsupervised clustering task into a supervised classification task [5]- [7]. First of all, an optimal quality clustering of unlabelled data needs to be obtained.…”

Section: Related Workmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

“…The authors in [7] develop the Single Feature Introduction Test (SFIT) method which is run on the model to recognise the statistically significant features which characterise each of the clusters of data. They test their discovered method on a real wealth management compliance case.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes

et al. 2022

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“…Incidentally, explainable clustering is an up-and-coming area of research that can be used to improve ML-based clustering models' completeness, maintainability, resilience, sensitivity, consistency, accuracy, and robustness. For example, in [171] authors have proposed a general interpretability framework for any clustering/classification model called a single feature introduction test (SFIT) to explain the clusters. Although these techniques are used for different use cases, there is potential for them to be applied anywhere (e.g., B5G security) as long as the underlying algorithms are compatible.…”

Section: E Security Of B5g E2e Slicingmentioning

confidence: 99%

A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases, Challenges and Research Directions

Senevirathna¹,

Salazar²,

La³

et al. 2022

Preprint

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With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks, from IoT devices and edge computing to cloud-based infrastructures. However, most of the existing surveys in B5G security focus on the performance of AI/ML models and their accuracy, but they often overlook the accountability and trustworthiness of the models' decisions. Explainable AI (XAI) methods are promising techniques that would allow system developers to identify the internal workings of AI/ML black-box models. The goal of using XAI in the security domain of B5G is to allow the decision-making processes of the security of systems to be transparent and comprehensible to stakeholders making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as RAN, zero-touch network management, E2E slicing, this survey emphasizes the role of XAI in them and the use cases that the general users would ultimately enjoy. Furthermore, we presented the lessons learned from recent efforts and future research directions on top of the currently conducted projects involving XAI.

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“…In [20] authors perform a two-step explanation procedure. First, they obtain cluster labels with arbitrary selected clustering mechanism, which later are used as the target variable in a classification task.…”

Section: Explainable Clusteringmentioning

confidence: 99%

“…Prior knowledge Explanation granularity [35] statistical summary, visualization model-agnostic no single instance [52] example-based model-specific no single instance [12] rules,trees model-specific no single instance [20] feature importance model-agnostic no global [15] rules model-specific no single instance [28][23], [4] [13] rules, trees model-specific no single-instance [16] rules, ontologies model-specific yes single instance [30] rules model-agnostic no single instance [27] rules model-agnostic no global [22] knowledge graph model-specific no global…”

Section: Reference Explanation Form Explanation Mechanismmentioning

confidence: 99%

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

Bobek¹,

Kuk²,

Brzegowski³

et al. 2021

Preprint

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Pattern discovery in multidimensional data sets has been a subject of research since decades. There exists a wide spectrum of clustering algorithms that can be used for that purpose. However, their practical applications share in common the post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be a bottleneck of the process, especially in the cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters, but also a conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KNAC), which main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution does not depend on any ready clustering algorithm, nor introduce one. Instead KNAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and model-agnostic. We demonstrate the feasibility of our method on artificially, reproducible examples and on a real life use case scenario.

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Explainable clustering and application to wealth management compliance

Cited by 11 publications

References 7 publications

Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes

Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes

A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases, Challenges and Research Directions

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

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