Toward Improving Re-coloring Based Clustering with Graph b-Coloring

Ogino, Hiroki; Yoshida, Tetsuya

doi:10.1007/978-3-642-15246-7_21

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…It was still restrictive in terms of the explored search space due to its greedy and sequential process. [Ogino, Yoshida, 2010] proposed a re-coloring algorithm to do non-greedy search for the admissible colors of vertices. But cluster validity indices are used in order to determine the "optimal" partition and the literature shows the limits of these indices.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED CHAPTER: Mobile-Based Recommendation System in Conferences

Hernández

Moreno

Omatu

2014

Advances in Intelligent Systems and Computing

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The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing. The publications within "Advances in Intelligent Systems and Computing" are primarily textbooks and proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and worldwide distribution. This permits a rapid and broad dissemination of research results.

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Section: Related Workmentioning

confidence: 99%

RETRACTED CHAPTER: Mobile-Based Recommendation System in Conferences

Hernández

Moreno

Omatu

2014

Advances in Intelligent Systems and Computing

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“…Various graph‐theoretic clustering approaches try to find subsets of vertices in a graph based on the edges among the vertices. Several methods utilizes graph coloring techniques (Guënoche, Hansen, and Jaumard 1991; Ogino and Yoshida 2010). Other methods are based on the flow or cut in graph, such as spectral clustering (Ng et al 2001; von Luxburg 2007).…”

Section: Semisupervised Clusteringmentioning

confidence: 99%

A Graph‐based Approach for Semisupervised Clustering

Yoshida

2012

Computational Intelligence

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This paper proposes a graph‐based approach for semisupervised clustering based on pairwise relations among instances. In our approach, the entire data set is represented as an edge‐weighted graph by mapping each data element (instance) as a vertex and connecting the instances by edges with their similarities. In order to reflect pairwise constraints on the clustering process, the graph is modified by contraction as it is known from general graph theory and the graph Laplacian in spectral graph theory. The graph representation enables us to deal with pairwise constraints as well as pairwise similarities over the same unified representation. By exploiting the constraints as well as similarities among instances, the entire data set is projected onto a subspace via the modified graph, and data clustering is conducted over the projected representation. The proposed approach is evaluated over several real‐world data sets. The results are encouraging and show that it is worthwhile to pursue the proposed approach.

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Cluster Analysis of Patients Suffering from Addictions

Combes

2014

Advances in Intelligent Systems and Computing

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Toward Improving Re-coloring Based Clustering with Graph b-Coloring

Cited by 6 publications

References 12 publications

RETRACTED CHAPTER: Mobile-Based Recommendation System in Conferences

RETRACTED CHAPTER: Mobile-Based Recommendation System in Conferences

A Graph‐based Approach for Semisupervised Clustering

Cluster Analysis of Patients Suffering from Addictions

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