K-means with Three different Distance Metrics

By Archana Singh, A. Yadav, A. Rana. 2013

394 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a630316f9c98839098747007753a9bb6d05f752e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yi Yang
2. Linchao Zhu
3. Yang He
4. Ping Liu
5. Wei Huang
6. Trevor Darrell
7. Chuang Gan
8. Li Fei-Fei

In Information Systems

1. Linchao Zhu
2. Yi Yang

In Signal Processing

1. Yang He
2. Wei Huang
3. Linchao Zhu
4. Yi Yang
5. Xuanyi Dong

Citations in the corpus

• DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (2022, methodology): “Therefore, the correlation between two nodes can be obtained by measuring the similarity between the data captured at these nodes, e.g. using the Minkowski distance (Singh et al., 2013).”
• Meta Filter Pruning to Accelerate Deep Convolutional Neural Networks (2019, methodology): “First, we utilize the Minkowski distance [27] as one of our choice.”