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Package Usage: pypi: clusim

Clustering simliarity
7 versions
Latest release: about 4 years ago
204 downloads last month

View more package details: https://packages.ecosyste.ms/registries/pypi.org/packages/clusim

View more repository details: https://repos.ecosyste.ms/hosts/GitHub/repositories/Hoosier-Clusters%2Fclusim

Dependent Repos 2

lodovicoazzini/unboxer
Heatmap Clustering to Understand the Misbehaviours Exposed by Automatically Generated Test Inputs
  • * requirements.txt

Size: 63.5 MB - Last synced: 10 months ago - Pushed: almost 2 years ago

007gzs/test
  • * requirements.txt

Size: 1.6 MB - Last synced: 5 months ago - Pushed: 5 months ago

sunfanyunn/vGraph
Official code for "vGraph: A Generative Model for Joint CommunityDetection and Node Representation Learning" (Neurips 2019)
  • * requirements.txt

Size: 869 KB - Last synced: 7 months ago - Pushed: 7 months ago

dpacassi/dynamic-event-detection-in-data-streams
  • * app/data_processing/requirements.txt

Size: 60.7 MB - Last synced: about 1 year ago - Pushed: about 4 years ago

wrongu/modularity
  • ==0.4 requirements.txt

Size: 1.71 MB - Last synced: about 1 month ago - Pushed: almost 2 years ago

junipertcy/bipartiteSBM
A Bayesian model+algorithm for community detection in bipartite networks
  • >=0.3.1 requirements.txt

Size: 4.1 MB - Last synced: over 1 year ago - Pushed: over 3 years ago

University-of-Windsor/NICASN
Discovering clusters in social networks is of fundamental and practical interest. This paper presents a novel clustering strategy for large-scale highly-connected social net- works. We propose a new hybrid clustering technique based on non-negative matrix fac- torization and independent component analysis for finding complex relationships among users of a large-scale network. We extract the important features of the network and then perform clustering on independent and important components of the network. Above this, we introduce a new k-means centroid initialization method by which we achieve higher performance. We apply our approach on four well-known social networks: Face- book, Twitter, Academia and Youtube. The experimental results show that our approach generally achieves better results in terms of the Silhouette coefficient compared to well- known clustering methods such as Hierarchical Louvain, Multiple Local Community detection, and k-means++. In general, our approach outperforms the state-of-the-art techniques when dealing with complex and highly-connected networks
  • ==0.4 requirements.txt

Size: 20 MB - Last synced: about 1 year ago - Pushed: about 1 year ago

letiziaia/multilayer-alignment
  • * Pipfile
  • ==0.4 Pipfile.lock

Size: 250 KB - Last synced: 3 months ago - Pushed: 3 months ago

zohdit/DeepAtash
  • ==0.4 IMDB/requirements.txt
  • ==0.4 MNIST/requirements.txt

Size: 610 MB - Last synced: 2 months ago - Pushed: 9 months ago