Model-based evaluation of clustering validation measures

Brun, Marcel; Sima, Chao; Jiang, Hua; Lowey, James; Carroll, B.R.; Suh, Edward; Dougherty, Edward R.

doi:10.1016/j.patcog.2006.06.026

Cited by 193 publications

(153 citation statements)

References 15 publications

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“…We agree with Dougherty and Brun [19,15] that "validation" of clustering results is a heuristic process, even though there are some interesting efforts to objectively incorporate biological knowledge in this process using Gene Ontology, especially when one is clustering gene expression profiles [17,23]. However, to illustrate the usefulness of our software, we collected several examples in which the performance of Simcluster can be considered as qualitatively superior to some traditional approaches imported from the microarray analysis field.…”

Section: Resultssupporting

confidence: 65%

Simcluster: clustering enumeration gene expression data on the simplex space

et al. 2007

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Transcript enumeration methods such as SAGE, MPSS, and sequencingby-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be noninformative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus

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Section: Resultssupporting

confidence: 65%

Simcluster: clustering enumeration gene expression data on the simplex space

et al. 2007

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“…62,63 Perhaps the oxide electronics challenges are rather better described as "small" and "deep" functionality-targeted data problems, where the necessary prior knowledge required to make progress still remains to be uncovered from hypothesis-and exploration-driven scientific discovery methodologies and personal communications between theorists and experimentalists. The inability of informatics-based data analyses alone to create robust, i.e., optimal model design and error estimation, mechanistic knowledge from small data sets, typical of what we can expect in oxides, has been borne out in biology and genetics; [64][65][66] similar pitfalls should be avoidable in materials science. The field should establish to what extent statistical inference and learning methods can benefit materials discovery by including authentic structure-property relationships guided by physical models.…”

Section: -3mentioning

confidence: 99%

Research Update: Towards designed functionalities in oxide-based electronic materials

2015

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“…Clustering validity measures fall broadly into three classes [13]: a) internal validation (based on properties of the resulting clusters), b) relative validation (running the algorithm with different parameters), c) external validation (comparison with a given partition of the data).…”

Section: Evaluation Of Clustering Resultsmentioning

confidence: 99%

“…Following the recommendation proposed in [13], we evaluate the clustering results based on silhouette values that seem to be a good internal validation measure and also provide good graphical representation of clustering quality. The silhouettes validation technique [14] calculates the silhouette width for each sample, the average silhouette width for each cluster and the overall average silhouette width for a total data set.…”

Section: Evaluation Of Clustering Resultsmentioning

confidence: 99%

SOM-Based Clustering and Optimization of Production

Potočnik

Berlec

Starbek

et al. 2011

IFIP Advances in Information and Communication Technology

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Abstract. An application of clustering methods for production planning is proposed. Hierarchical clustering, k-means and SOM clustering are applied to production data from the company KGL in Slovenia. A database of 252 products manufactured in the company is clustered according to the required operations and product features. Clustering results are evaluated with an average silhouette width for a total data set and the best result is obtained by SOM clustering. In order to make clustering results applicable to industrial production planning, a percentile measure for the interpretation of SOM clusters into the production cells is proposed. The results obtained can be considered as a recommendation for production floor planning that will optimize the production resources and minimize the work and material flow transfer between the production cells.

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Model-based evaluation of clustering validation measures

Cited by 193 publications

References 15 publications

Simcluster: clustering enumeration gene expression data on the simplex space

Simcluster: clustering enumeration gene expression data on the simplex space

Research Update: Towards designed functionalities in oxide-based electronic materials

SOM-Based Clustering and Optimization of Production

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