We study this problem from a theoretical viewpoint, measuring cluster quality by the $k$-means and $k$-medians objectives: Must there exist a tree-induced clustering whose cost is comparable to that of the best unconstrained clustering, and if so, how can it be found? In terms of ...
Both k-means and k-medoids require the user to specify [Math Processing Error], the expected number of clusters, and this is often not easy in practice. Another distance-based clustering method is Principal Direction Divisive Partitioning (PDDP) by Boley [14], which proceeds by repeatedly ...
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MEC itself can be expected to be a trusted infrastructure, with virtual machine/container isolation ensured through standard OS security means [45]. Likewise, transmission over the RAN and the core network of the mobile operator is expected to be secure and private through authentication and ...
Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those
8636 Accesses 7 Citations 1 Altmetric Explore all metrics Abstract As the range of decisions made by Artificial Intelligence (AI) expands, the need for Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific outcomes of complex and opaque financial models requires a tho...
algorithm that outputs an explainable clustering that loses at most a factor of O(\log^2 k) O(\log^2 k) compared to an optimal (not necessarily explainable) clustering for the k k -medians objective, and a factor of O(k \log^2 k) O(k \log^2 k) for the k k -means objective...
Globalbehavior is often too complex to visualize or comprehend, so approxima-tions are shown, and visualizing local behavior is often misleading as itis difficult to define what local exactly means (i.e. our methods don’t“know” how easily a feature-value can be changed; which ones are ...
The x axis indicates the SHAP value where a positive SHAP value means a higher contribution to the fault and a negative value means a negative impact on the fault. For SHAP, the summary plot is made up of SHAP values from each individual sample. Therefore, this interpretation also represents...
Methods such as k-means can be applied to the training set to observe the initial state of the input space [23]. Uninformative observation can be removed from the data set to avoid class imbalance [66] before building the ML model in the “Model engineering” phase. The “Learned features...