ExKMC: Expanding Explainable $k$-Means Clustering

Frost, Nave; Moshkovitz, Michal; Rashtchian, Cyrus

doi:10.48550/arxiv.2006.02399

Cited by 10 publications

(16 citation statements)

References 36 publications

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“…In addition to that, authors of [172] have proposed using decision trees to interpret the clusters provided by k-means and k-median clustering algorithms. ExKMC technique introduced in [173] is another approach to add the interpretability of the k-means clustering technique. It follows a similar approach as in [173] with small decision trees modified k-means algorithm.…”

Section: E Security Of B5g E2e Slicingmentioning

confidence: 99%

“…ExKMC technique introduced in [173] is another approach to add the interpretability of the k-means clustering technique. It follows a similar approach as in [173] with small decision trees modified k-means algorithm. Since slicing is focused on delivering customized services to consumers, transparency is a growing concern in the community.…”

Section: E Security Of B5g E2e Slicingmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: E Security Of B5g E2e Slicingmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…We warm-start the trees using the optimal k-mean clustering with the optimal decision tree, set the maximum depth to 3, a minimum bucket size of 1, and use a geometric search threshold of 0.99. • Explainable K-Means Clustering (ExKMC): ExKMC is an algorithm that uses decision trees to explain the output of k-means clustering (Frost, Moshkovitz, and Rashtchian 2020). We present two version of ExKMC results -one where we restrict each cluster to have a single leaf node (i.e.…”

Section: B Experiments Detailsmentioning

confidence: 99%

Interpretable Clustering via Multi-Polytope Machines

Lawless¹,

Kalagnanam²,

Nguyen³

et al. 2021

Preprint

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Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description -few stateof-the-art algorithms provide any rationale or description behind the clusters found. We propose a novel approach for interpretable clustering that both clusters data points and constructs polytopes around the discovered clusters to explain them. Our framework allows for additional constraints on the polytopes -including ensuring that the hyperplanes constructing the polytope are axis-parallel or sparse with integer coefficients. We formulate the problem of constructing clusters via polytopes as a Mixed-Integer Non-Linear Program (MINLP). To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance. We benchmark our approach on a suite of synthetic and real world clustering problems, where our algorithm outperforms state of the art interpretable and non-interpretable clustering algorithms.

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“…Explainable k-means and k-medians clustering have also been studied in practice. Frost et al [13] and Laber and Murtinho [19] provided practical algorithms for explainable clustering evaluated on real datasets. Other results has also been developed for creating interpretable clustering models or clustering models based on decision trees [3,4,12,21,23].…”

Section: Other Related Workmentioning

confidence: 99%

Almost Tight Approximation Algorithms for Explainable Clustering

Esfandiari¹,

Mirrokni²,

Narayanan³

2021

Preprint

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Recently, due to an increasing interest for transparency in artificial intelligence, several methods of explainable machine learning have been developed with the simultaneous goal of accuracy and interpretability by humans. In this paper, we study a recent framework of explainable clustering first suggested by Dasgupta et al. [11]. Specifically, we focus on the k-means and k-medians problems and provide nearly tight upper and lower bounds. First, we provide an O(log k log log k)-approximation algorithm for explainable k-medians, improving on the best known algorithm of O(k) [11] and nearly matching the known Ω(log k) lower bound [11]. In addition, in low-dimensional spaces d ≪ log k, we show that our algorithm also provides an O(d log 2 d)-approximate solution for explainable k-medians. This improves over the best known bound of O(d log k) for low dimensions [19], and is a constant for constant dimensional spaces. To complement this, we show a nearly matching Ω(d) lower bound. Next, we study the k-means problem in this context and provide an O(k log k)-approximation algorithm for explainable k-means, improving over the O(k 2 ) bound of Dasgupta et al. and the O(dk log k) bound of [19]. To complement this we provide an almost tight Ω(k) lower bound, improving over the Ω(log k) lower bound of Dasgupta et al. Given an approximate solution to the classic kmeans and k-medians, our algorithm for k-medians runs in time O(kd log 2 k) and our algorithm for k-means runs in time O(k 2 d).

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ExKMC: Expanding Explainable $k$-Means Clustering

Cited by 10 publications

References 36 publications

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

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

Interpretable Clustering via Multi-Polytope Machines

Almost Tight Approximation Algorithms for Explainable Clustering

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