Validation indices for graph clustering

Günter, Simon; Bunke, Horst

doi:10.1016/s0167-8655(02)00257-x

Cited by 127 publications

(64 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many authors did not even consider the algorithm correctness assumption and they carried out the experiment without checking if the P nc partitions obtained were correct or were at least the best partitions the algorithms used could propose (Günter and Bunke, 2003;Kim and Ramakrishna, 2005;Maulik and Bandyopadhyay, 2002;Pal and Biswas, 1997). This type of work may be valid and the conclusions drawn from it may be correct, but we cannot be confident about the results obtained without a validation step.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it is clear that a method for comparing different CVIs, which we call an evaluation of cluster validity indices, is necessary. Although little theoretical work has been done in this context, most CVI evaluation work has followed the same methodology (Chou et al, 2004;Devillez et al, 2002;Günter and Bunke, 2003;Kim and Ramakrishna, 2005;Maulik and Bandyopadhyay, 2002;Milligan and Cooper, 1985;Pal and Biswas, 1997). We call this methodology the classical methodology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards a standard methodology to evaluate internal cluster validity indices

Gurrutxaga

Muguerza

Arbelaitz

et al. 2011

Pattern Recognition Letters

View full text Add to dashboard Cite

The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both methodologies and concluded that the results obtained with the proposed methodology are more representative of the actual capabilities of the compared indices.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a standard methodology to evaluate internal cluster validity indices

Gurrutxaga

Muguerza

Arbelaitz

et al. 2011

Pattern Recognition Letters

View full text Add to dashboard Cite

show abstract

“…The following clustering evaluation measures are often used: Silhouette index, Dunn's index, Davies-Bouldin index, etc. [2,10]. In order to present the definition of the Davies-Bouldin index, we first have to define the inter-cluster and intra-cluster distance.…”

Section: The New Labelling Algorithmmentioning

confidence: 99%

“…Stability of results: this index is less sensitive to the position of a small group of data set members (so called outliers) than other measures, such as for example, the Dunn's index [10].…”

Section: Intra-cluster Distancementioning

confidence: 99%

Labelling Clusters in an Intrusion Detection System Using a Combination of Clustering Evaluation Techniques

Petrović

Álvarez²,

Orfila

et al. 2006

Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)

View full text Add to dashboard Cite

A new clusters labelling strategy, which combines the computation of the Davies-Bouldin index of the clustering and the centroid diameters of the clusters is proposed for application in anomaly based intrusion detection systems (IDS). The aim of such a strategy is to detect compact clusters containing very similar vectors and these are highly likely to be attack vectors. Experimental results comparing the effectiveness of a multiple classifier IDS with such a labelling strategy and that of the classical cardinality labelling based IDS show that the proposed strategy behaves much better in a heavily

show abstract

“…Cluster validity techniques include the Silhouette method [7], Dunn's based index [8,9], Davies-Bouldin index [10] and the C-index [11]. For a review on validation techniques the reader is referred to [12].…”

Section: Introductionmentioning

confidence: 99%

Cluster validation techniques for genome expression data

2003

View full text Add to dashboard Cite

Several clustering algorithms have been suggested to analyse genome expression data, but fewer solutions have been implemented to guide the design of clusteringbased experiments and assess the quality of their outcomes. A cluster validity framework provides insights into the problem of predicting the correct the number of clusters. This paper presents several validation techniques for gene expression data analysis. Normalisation and validity aggregation strategies are proposed to improve the prediction about the number of relevant clusters. The results obtained indicate that this systematic evaluation approach may significantly support genome expression analyses for knowledge discovery applications.

show abstract

Validation indices for graph clustering

Cited by 127 publications

References 10 publications

Towards a standard methodology to evaluate internal cluster validity indices

Towards a standard methodology to evaluate internal cluster validity indices

Labelling Clusters in an Intrusion Detection System Using a Combination of Clustering Evaluation Techniques

Cluster validation techniques for genome expression data

Contact Info

Product

Resources

About