On the Comparison of Relative Clustering Validity Criteria

Vendramin, Lucas; Campello, Ricardo J. G. B.; Hruschka, Eduardo R.

doi:10.1137/1.9781611972795.63

Cited by 100 publications

(90 citation statements)

References 20 publications

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“…This can be done by using some of the well-known validity indexes (see e.g. [13,23]). In our paper, we will use the Calinski-Harabasz (CH) index and the Davies-Bouldin (DB) index.…”

Section: Choosing Of a Partition With The Most Appropriate Number Of mentioning

confidence: 99%

Research project grouping and ranking by using adaptive Mahalanobis clustering

Turkalj

Markulak

Singer

et al. 2016

Croat. Oper. Res. Rev.

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Abstract. The paper discusses the problem of grouping and ranking of research projects submitted for a call. The projects are grouped into clusters based on the assessment obtained in the review procedure and by using the adaptive Mahalanobis clustering method as a special case of the Expectation Maximization algorithm. The cluster of projects assessed as best is specially analyzed and ranked. The paper outlines several possibilities for the use of data obtained in the review procedure, and the proposed method is illustrated with the example of internal research projects at the University of Osijek.

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“…This can be done by using some of the well-known validity indexes (see e.g. [13,23]). In our paper, we will use the Calinski-Harabasz (CH) index and the Davies-Bouldin (DB) index.…”

Section: Choosing Of a Partition With The Most Appropriate Number Of mentioning

confidence: 99%

Research project grouping and ranking by using adaptive Mahalanobis clustering

Turkalj

Markulak

Singer

et al. 2016

Croat. Oper. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…5.5.2) was chosen as I D to evaluate the quality of the partitions produced by the DOMR procedure. DFSS is the distributed fuzzy version of the Simplified Silhouette index [62], which scored the best among 40 indices in a recent comparative study [52,74]. The experiments were performed in a parallel scenario, where the responsibility for processing predetermined sub-collections of the data is distributed across the different cores of a single processor, which can then be seen as virtually distributed data sites, even though in this parallel computing setup the data is stored in a shared memory that is accessible by all the cores.…”

Section: 7 Experimental Evaluationmentioning

confidence: 99%

Fuzzy Clustering Algorithms and Validity Indices for Distributed Data

Vendramin

Naldi

Campello

2014

Partitional Clustering Algorithms

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This chapter presents a unified framework to generalize a number of fuzzy clustering algorithms to handle distributed data in an exact way, i.e., with no approximation of results with respect to their original centralized versions. The same framework allows the exact distribution of relative validity indices used to evaluate the quality of fuzzy clustering solutions. Complexity analyses for each distributed algorithm and index are reported in terms of space, time, and communication aspects. A general procedure to estimate the number of clusters in a non-centralized fashion using the proposed framework is also described. Such a procedure is directly applicable not only to distributed data, but to parallel data processing scenarios as well. Experimental results illustrate the speedup obtained when running algorithms under the proposed framework in multiple cores of a processor, when compared to their traditional, centralized counterparts running in a single core. Additionally, the quality of the results and amount of data transmitted are assessed and compared among different fuzzy clustering algorithms.

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“…Em estudos comparativos recentes (Vendramin et al, 2009(Vendramin et al, , 2010, o índice da Silhueta se desempenhou melhor que dezenas de outros índices relativos e internos projetados para agrupamentos particionais exclusivos. O índice da Silhueta é descrito na seção seguinte.…”

Section: Validação De Agrupamentounclassified

“…Essa modificação reduz a complexidade computacional do índice da Silhueta, O(n 2 ), para O(nk), em que n é o número de objetos da base e k é o número de grupos. A versão simplificada apresentou resultados similares aos da versão original em extensivos estudos comparativos (Vendramin et al, 2009(Vendramin et al, , 2010.…”

Section: Silhueta Simplificadaunclassified

“…O índice da silhueta simplificada (Hruschka et al, 2004, usado em validação relativa de agrupamento, tem apresentado um desempenho destacado em estudos comparativos recentes (Vendramin et al, 2008(Vendramin et al, , 2009(Vendramin et al, , 2010, o que, em conjunto com a Hipótese 2, motivou o Objetivo 2: Desenvolver uma versão da silhueta simplificada que considere a ponderação de representações associada ao agrupamento. Compará-la com a versão sem ponderação.…”

Section: Introductionunclassified

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

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On the Comparison of Relative Clustering Validity Criteria

Cited by 100 publications

References 20 publications

Research project grouping and ranking by using adaptive Mahalanobis clustering

Research project grouping and ranking by using adaptive Mahalanobis clustering

Fuzzy Clustering Algorithms and Validity Indices for Distributed Data

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

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