International audienceUnderstanding the structure of relationships between objects in a given database is one of the most important problems in the field of data mining. The structure can be defined for a set of single objects (clustering) or a set of groups of objects (network mapping). We propose a method for discovering relationships between individuals (single or groups) that is based on what we call the empirical topology, a system-theoretic measure of functional proximity. To illustrate the suitability and efficiency of the method, we apply it to an...
Functional networks, i.e. networks representing the interactions between the elements of a complex s...
The statistical mechanical approach to complex networks is the dominant paradigm in describing natur...
In this paper, we develop a novel framework for defining radial measures of centrality in complex ne...
This work proposes a method for data clustering based on complex networks theory. A data set is repr...
AbstractThis work proposes a method for data clustering based on complex networks theory. A data set...
Networks have become a general concept to model the structure of arbitrary relationships among entit...
Clustering is one of the most used data mining techniques, while computational topology is a very re...
Data mining techniques have an important implication in social and biological network analysis, were...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
The current study was a first step exploration of a new method that used mutual information-based me...
International audienceMining relational data often boils down to computing clusters, that is finding...
The concept of a cluster or community in a network context has been of considerable interest in a va...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
In this paper we present observers with point patterns based on 30 major star constellations and ask...
Over the past few decades Social Network Analysis has found increasing application in many social re...
Functional networks, i.e. networks representing the interactions between the elements of a complex s...
The statistical mechanical approach to complex networks is the dominant paradigm in describing natur...
In this paper, we develop a novel framework for defining radial measures of centrality in complex ne...
This work proposes a method for data clustering based on complex networks theory. A data set is repr...
AbstractThis work proposes a method for data clustering based on complex networks theory. A data set...
Networks have become a general concept to model the structure of arbitrary relationships among entit...
Clustering is one of the most used data mining techniques, while computational topology is a very re...
Data mining techniques have an important implication in social and biological network analysis, were...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
The current study was a first step exploration of a new method that used mutual information-based me...
International audienceMining relational data often boils down to computing clusters, that is finding...
The concept of a cluster or community in a network context has been of considerable interest in a va...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
In this paper we present observers with point patterns based on 30 major star constellations and ask...
Over the past few decades Social Network Analysis has found increasing application in many social re...
Functional networks, i.e. networks representing the interactions between the elements of a complex s...
The statistical mechanical approach to complex networks is the dominant paradigm in describing natur...
In this paper, we develop a novel framework for defining radial measures of centrality in complex ne...