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```
What steps will reproduce the problem?
1. Go to search screen i.e. searchindatabase.fig file
2. Select the search using
3. Search using DCT domain a.k.a clustering techniques
The results should …
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# Abstract
Q-means is an unsupervised machine learning approach that is the quantum version of of the K-means clustering algorithm. The proposed q-means offers an exponential speedup in the number of…
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### Is there an existing issue for this?
- [X] I have searched the existing issues
### Feature Description
Identify and visualize areas with higher occurrences of harassment inciden
### Use Case
…
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This is the algorithm from #127
The demo should include:
- Running the notebook
- Explaining the steps in the notebook
- Explaining what insights are we gaining from the clustering
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Helloc @vtraag,
Leiden seems to be built like Louvain, in the sense that in the process of finding the best partition the nodes within a community are merged into a metanode, before performing agai…
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Hello. Thanks for the great work.
Instead of finetuning with k-shot, I want to finetune with the entire DIOR dataset(DIOR_train). What should I do?
First, I tried to create a .pkl file for DIOR_tr…
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Hi brendaf
I have a dataset of ~140,000 ASVs that I want to tun through optimotu. The ASVs have been assigned taxonomy. Do you have any examples or recommendations for preparing the ASVs before run…
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Currently, these are the different workflow steps for Mobility Analysis:
- Incremental Clustering
- Trace Segmentation Clustering
- Address Oscillation (Oscillation Collector)
- Update Stay Dura…
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Hello!
Would you be interested in MeanShift implementation? I'm planning on using it as clustering algorithm in my project, and would like to contribute to Linfa as well 🦀
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When dealing with large datasets and memory constraints, one popular clustering algorithm that can be effective is the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. D…