A method of relational fuzzy clustering based on producing feature vectors using FastMap

Brouwer, Roelof K.

doi:10.1016/j.ins.2009.06.018

Cited by 19 publications

(10 citation statements)

References 69 publications

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“…In spite of the fact that many relational fuzzy clustering algorithms have been developed already (Roubens, 1978;Windham, 1985;Hathaway et al, 1989;Hathaway and Bezdek, 1994;Bezdek et al, 1999;Inoue and Urahama, 1999;Yang and Shih, 2001;Davé and Sen, 2002;Brouwer, 2009), they all involve manually specified parameters such as the number of clusters or threshold of similarity without providing any guidance for choosing them, which is a weakness to develop decision-support or expert systems. Indeed, the determination of the number of clusters in data is a fundamental problem in cluster analysis (see Mirkin (2011a) for a state-of-the-art perspective).…”

Section: Introductionmentioning

confidence: 99%

“…One of these fuzzy clustering algorithms combines fuzzy c-means with a recently proposed fast-mapping technique proved superior to many other techniques, the Fast Map Fuzzy c-Means (FMFCM) (Brouwer, 2009), and the other is an extension of the c-means to dissimilarity data, the Non-Euclidean Relational Fuzzy c-Means (NERFCM) (Hathaway and Bezdek, 1994). One of these fuzzy clustering algorithms combines fuzzy c-means with a recently proposed fast-mapping technique proved superior to many other techniques, the Fast Map Fuzzy c-Means (FMFCM) (Brouwer, 2009), and the other is an extension of the c-means to dissimilarity data, the Non-Euclidean Relational Fuzzy c-Means (NERFCM) (Hathaway and Bezdek, 1994).…”

Section: Introductionmentioning

confidence: 99%

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Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach

2013

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This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, Fuzzy Additive Spectral clustering (FADDIS), recently introduced by authors. Specifically, we extend our analysis to ‘difficult’ data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state‐of‐the‐art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested, and the role of the pseudo‐inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach

2013

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“…Since the fuzzy set theory was proposed in 1965 by Zadeh [36,37], fuzzy clustering as a fundamental tool has been applied to many fields extensively [3][4][5]9,19]. In this paper, we provide a novel method based on fuzzy clustering to detect community structure in complex networks.…”

Section: Introductionmentioning

confidence: 99%

Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks

Sun

Gao

Han

2011

Information Sciences

108

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“…Because of this existing problem in emotional modeling research areas, eMCM and eHMM have been proposed [8], the multidimensional scaling method is adopted to reveal the changes in emotion caused by adjusting parameters. This method is commonly used in the areas of sociology, economics, information science [11][12][13][14][15][16][17][18]. Section 2 of this paper defines and describes two transfer models of emotion state, spontaneous transfer, and stimulated transfer.…”

Section: Introductionmentioning

confidence: 99%

Individual difference of artificial emotion applied to a service robot

Wang

Zheng

et al. 2011

Front. Comput. Sci. China

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In order to enable personalized natural interaction in service robots, artificial emotion is needed which helps robots to appear as individuals. In the emotion modeling theory of emotional Markov chain model (eMCM) for spontaneous transfer and emotional hidden Markov model (eHMM) for stimulated transfer, there are three problems: 1) Emotion distinguishing problem: whether adjusting parameters of the model have any effects on individual emotions; 2) How much effect the change makes; 3) The problem of different initial emotional states leading to different resultant emotions from a given stimuli. To solve these problems, a research method of individual emotional difference is proposed based on metric multidimensional scaling theory. Using a dissimilarity matrix, a scalar product matrix is calculated. Subsequently, an individual attribute reconstructing matrix can be obtained by principal component factor analysis. This can display individual emotion difference with low dimension. In addition, some mathematical proofs are carried out to explain experimental results. Synthesizing the results and proofs, corresponding conclusions are obtained. This new method provides guidance for the adjustment of parameters of emotion models in artificial emotion theory.

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A method of relational fuzzy clustering based on producing feature vectors using FastMap

Cited by 19 publications

References 69 publications

Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach

Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach

Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks

Individual difference of artificial emotion applied to a service robot

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