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Cluster analysis, graphs, and branching processes as new methodologies for intelligent systems on example of bibliometric and social network data
Author(s) -
Nowakowska Maria
Publication year - 1990
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550050303
Subject(s) - computer science , formalism (music) , inference , cluster (spacecraft) , theoretical computer science , artificial intelligence , branching (polymer chemistry) , artificial neural network , data science , network analysis , data mining , machine learning , materials science , composite material , programming language , art , musical , physics , quantum mechanics , visual arts
This article presents (1) a general formalism for cluster analysis, allowing a systemic study of simulation research, in particular its dynamic aspects, (2) a model of small bibliographical clusters, allowing inference (among others) on the connectivity of domains, and (3) an outline of new theories of networks with randomly changing nodes and edges, applicable for analysis of different types of relations, e.g., communication between scientists, etc. These models may be useful for analysis of large databases in artificial intelligence. They may also have significance as new approaches to neural network analysis.