Nerf c-means: Non-Euclidean relational fuzzy clustering

Hathaway, Richard J.; Bezdek, James C.

doi:10.1016/0031-3203(94)90119-8

Cited by 288 publications

(174 citation statements)

References 7 publications

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“…Some of the early fuzzy relational clustering algorithms are introduced in [11], [12] and [18][19]. The Relational Fuzzy C-Means (RFCM) [12] is extended in [21] to release the restrictions that RFCM requires on the dissimilarity matrix. More robust approach is found in [19].…”

Section: Introductionmentioning

confidence: 99%

AVLINK: Robust Clustering Algorithm based on Average Link Applied to Protein Sequence Analysis

Mahfouz¹

2016

JMSS

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Robust Clustering methods are aimed at avoiding unsatisfactory results resulting from the presence of certain amount of outlying observations in the input data of many practical applications such as biological sequences analysis or gene expressions analysis. This paper presents a fuzzy clustering algorithm based on average link and possibilistic clustering paradigm termed as AVLINK. It minimizes the average dissimilarity between pairs of patterns within the same cluster and at the same time the size of a cluster is maximized by computing the zeros of the derivative of proposed objective function. AVLINK along with the proposed initialization procedure show a high outliers rejection capability as it makes their membership very low furthermore it does not requires the number of clusters to be known in advance and it can discover clusters of non convex shape. The effectiveness and robustness of the proposed algorithms have been demonstrated on different types of protein data sets.

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Section: Introductionmentioning

confidence: 99%

AVLINK: Robust Clustering Algorithm based on Average Link Applied to Protein Sequence Analysis

Mahfouz¹

2016

JMSS

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“…The primary mathematical goal is often to cluster or visualize data, such that an underlying structure becomes apparent. Quite a few approaches for unsupervised learning for structures based on general dissimilarities have been proposed in the past: kernel clustering techniques such as kernel self-organizing maps (SOM) or kernel neural gas (NG) [34,24] or relational clustering such as proposed for fuzzy-k-means, SOM, NG, or the generative topographic mapping (GTM) [13,7,8]. Further, many state-of-the art nonlinear visualization techniques such as t-distributed stochastic neighbor embedding are based on pairwise dissimilarities rather than vectors [31,15].…”

Section: Introductionmentioning

confidence: 99%

How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?

Mokbel

Groß

Lux

et al. 2012

Artificial Neural Networks in Pattern Recognition

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For complex data sets, the pairwise similarity or dissimilarity of data often serves as the interface of the application scenario to the machine learning tool. Hence, the final result of training is severely influenced by the choice of the dissimilarity measure. While dissimilarity measures for supervised settings can eventually be compared by the classification error, the situation is less clear in unsupervised domains where a clear objective is lacking. The question occurs, how to compare dissimilarity measures and their influence on the final result in such cases. In this contribution, we propose to use a recent quantitative measure introduced in the context of unsupervised dimensionality reduction, to compare whether and on which scale dissimilarities coincide for an unsupervised learning task. Essentially, the measure evaluates in how far neighborhood relations are preserved if evaluated based on rankings, this way achieving a robustness of the measure against scaling of data. Apart from a global comparison, local versions allow to highlight regions of the data where two dissimilarity measures induce the same results.

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“…Os valores de proximidade contidos em R podem ser ou de similaridade ou de dissimilaridade, mas não de ambos ao mesmo tempo, e devem respeitar algumas propriedades. Por motivos de adequação ao presente trabalho, é adotada a definição de matriz relacional fornecida por Hathaway e Bezdek (1994), na qual R é uma matriz de dissimilaridades que obedece as seguintes restrições:…”

Section: Fundamentaçãounclassified

Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento

Horta¹

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Nerf c-means: Non-Euclidean relational fuzzy clustering

Cited by 288 publications

References 7 publications

AVLINK: Robust Clustering Algorithm based on Average Link Applied to Protein Sequence Analysis

AVLINK: Robust Clustering Algorithm based on Average Link Applied to Protein Sequence Analysis

How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?

Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento

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