InfoQ

Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Martin Kleppmann, an associate professor at ...
IEEE

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Abstract: In traditional fault diagnosis methods in power systems, it is difficult to accurately classify and predict the types of faults. With the emergence of big data technology, the fault ...
IEEE

Kernel-Based Distance Metric Learning for Supervised

Abstract: Finding an appropriate distance metric that accurately reflects the (dis)similarity between examples is a key to the success of k-means clustering. While it is not always an easy task to ...
ons.gov.uk

Clustering similar local authorities in the UK, methodology

Cookies are small files stored on your device when you visit a website. We use some essential cookies to make this website work. We would like to set additional cookies to remember your settings and ...
acm.org

Theoretical Analysis of Edit Distance Algorithms

Edit distance—a classical problem in computer science—has received ongoing attention from both practitioners and theoreticians. Given two strings A and B, the edit distance is the minimum number of ...
Nature

Research on stress curve clustering algorithm of Fiber Bragg grating sensor

Fiber Bragg sensor (FBG) was widely used in national defense, railway, chemical industry, environment, nuclear power, bridge and tunnel monitoring and other fields due to its characteristics of low ...
InfoWorld

14 popular AI algorithms and their uses

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...
Frontiers

ProgClust: A progressive clustering method to identify cell populations

Identifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations ...
GitHub

k-means clustering algorithm implementation written in Go

k-means clustering partitions a multi-dimensional data set into k clusters, where each data point belongs to the cluster with the nearest mean, serving as a prototype of the cluster.
C&EN

Artificial Intelligence in Meta-optics

Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077 Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong ...
Frontiers

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

The flow field obtained from streamline simulation reflects key properties of the reservoir, such as the distribution of the remaining oil and the location of channels. However, in the ...