Name
Abstract | ||
|---|---|---|
The search for cohesive groups inside a social network is a topic commonly known as community detection and has attracted many researchers. However, the identification of groups with competitive features using blockmodeling, biclustering and structural or regular equivalences has benefited from a less important interest within the research community. In this paper we define a generic biclustering method called BiP that computes semantically meaningful coclusters (or biclusters) from a two-mode data network. The method has the following features: (i) it allows the processing of adjacency matrices whose data type can be either binary, discrete or categorical without any need for prior data codification, and (ii) each generated block may either represent a cluster of objects with the properties they own (or not), or a juxtaposition of sub-groups with distinct profiles (and sometimes purely opposed ones) for a subset of attributes.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.5555/3192424.3192564 | ASONAM '16: Advances in Social Networks Analysis and Mining 2016
Davis
California
August, 2016 |
Field | DocType | ISBN |
Adjacency matrix,Data mining,Algorithm design,Computer science,Visualization,Categorical variable,Data type,Artificial intelligence,Biclustering,Cluster analysis,Machine learning,Binary number | Conference | 978-1-5090-2846-7 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdélilah Balamane | 1 | 0 | 0.68 |
| Rokia Missaoui | 2 | 983 | 136.45 |
| Léonard Kwuida | 3 | 55 | 16.25 |
| Jean Vaillancourt | 4 | 25 | 5.71 |