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Book ChapterDOI

A continuous-based approach for partial clique enumeration

TLDR
This paper presents a novel approach for partial clique enumeration, that is, the extraction of the K largest cliques of a graph, based on a continuous formulation of the clique problem developed by Motzkin and Straus, and is able to avoid extracting the same clique multiple times.
Abstract
In many applications of computer vision and pattern recognition which use graph-based knowledge representation, it is of great interest to be able to extract the K largest cliques in a graph, but most methods are geared either towards extracting the single clique of maximum size, or enumerating all cliques, without following any particular order. In this paper we present a novel approach for partial clique enumeration, that is, the extraction of the K largest cliques of a graph. Our approach is based on a continuous formulation of the clique problem developed by Motzkin and Straus, and is able to avoid extracting the same clique multiple times. This is done by casting the problem into a gametheoretic framework and iteratively rendering unstable the solutions that have already been extracted.

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Citations
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Journal ArticleDOI

A generalization of the Motzkin–Straus theorem to hypergraphs

TL;DR: This paper provides a generalization of the Motzkin–Straus theorem to k-uniform hypergraphs (k-graphs) and exhibits a family of (parameterized) homogeneous polynomials whose local minimizers are shown to be in one-to-one correspondence with maximal (maximum) cliques of G.
Posted Content

Interactive Image Segmentation Using Constrained Dominant Sets

TL;DR: By properly controlling a regularization parameter, this work shows that by properly controlling the structure and the scale of the underlying problem, it is in a position to extract groups of dominant-set clusters which are constrained to contain user-selected elements.
Journal ArticleDOI

Matching of anatomical tree structures for registration of medical images

TL;DR: This paper proposes a method which is able to find reasonable landmarks automatically and achieves promising results for anatomical trees of liver and lung and for medical images obtained with different modalities and at different points in time.
Posted Content

Enumeration of Enumeration Algorithms.

TL;DR: In this paper, enumerate enumeration problems and algorithms are enumerated and proposed algorithms for solving these problems are described.
Dissertation

A Game-Theoretic Framework for Similarity-Based Data Clustering

TL;DR: This thesis introduces an approach for enumerating equilibria of two-player symmetric games, by iteratively rendering unstable already visited ones through a particular asymmetric extension of the payoff matrix and proposes a dynamics falling in this class, which allows to overcome the problems afflicting standard evolutionary dynamics.
References
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Book

Evolutionary Game Theory

TL;DR: Weibull as discussed by the authors introduces evolutionary game theory, where ideas from evolutionary biology and rationalistic economics meet, emphasizing the links between static and dynamic approaches and non-cooperative game theory.
Proceedings ArticleDOI

Automatic subspace clustering of high dimensional data for data mining applications

TL;DR: CLIQUE is presented, a clustering algorithm that satisfies each of these requirements of data mining applications including the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records.
Book ChapterDOI

The maximum clique problem

TL;DR: A survey of results concerning algorithms, complexity, and applications of the maximum clique problem is presented and enumerative and exact algorithms, heuristics, and a variety of other proposed methods are discussed.
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