Abstract: Data stream clustering reveals patterns within continuously arriving, potentially unbounded data sequences. Numerous data stream algorithms have been proposed to cluster data streams. The ...

The Legacy Survey of Space and Time (LSST) will provide a ground-breaking data set for cosmology, but to achieve the precision needed, the data, data reduction, and algorithms measuring the ...

There is a strong association between vascular risk factors, particularly in midlife, and stroke risk; therefore, the co-occurrence of multiple risk factors may be especially informative. This study ...

When requesting logs from the Diagnostic Logs cluster through BDX from the Python controller using RetrieveLogsRequest, if two BDX transfers are requested back to back with no delay in between the ...

Introduction: The relationship between physical activity and anxiety among students has been extensively studied, with research highlighting the protective effects of physical activity on mental ...

Autoimmune conditions and ‘breast implant illness’ in breast cancer patients with implant-based breast reconstructions. Proportions of patients with clinically meaningful symptoms by CL at Y1 (may not ...

In this tutorial, we will discover how to harness the power of an advanced AI Agent, augmented with both Python execution and result-validation capabilities, to tackle complex computational tasks. By ...

K-Means Clustering Overview K-Means aims to partition your data into K distinct, non-overlapping clusters based on similarity. It minimizes the within-cluster sum of squares (WCSS) — i.e., how close ...

In this study, our focus is on implementing clustering methods to rearrange time series data that represent technological proximity based on their shapes across various index types. The objective of ...

Introduction: Mass gathering activities are often accompanied by safety risks especially overcrowding, since the characteristics of risk factors may differ under various organizational patterns.