Efficient clustering of large uncertain graphs using neighborhood information

DOI10.1016/j.ijar.2017.07.013zbMath1419.68166OpenAlexW2742861380MaRDI QIDQ1678437

Muhammad Waqas, Abdul Rauf Baig, Ahmar Rashid, Zahid Halim

Publication date: 17 November 2017

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2017.07.013

zbMATH Keywords

clusteringgraph-based clusteringheuristic clustering algorithmuncertain graphs

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Graph theory (including graph drawing) in computer science (68R10) Reasoning under uncertainty in the context of artificial intelligence (68T37) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (3)

Effective and efficient aggregation on uncertain graphs ⋮ Clustering probabilistic graphs using neighbourhood paths ⋮ Multi-granularity principal curves extraction based on improved spectral clustering of complex distribution data

Cites Work

• An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification
• Agglomerative clustering via maximum incremental path integral
• A survey of kernel and spectral methods for clustering
• 10.1162/153244302760185234
• Fast unfolding of communities in large networks
• Overlapping community detection in networks
• Clustering Social Networks
• Aggregating inconsistent information
• On clustering validation techniques

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