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About 10,000 papers for “cluster validation”

Cluster validation.

Marco Borri (799311), Maria A. Schmidt (799312), Ceri Powell (799313) et al. · Figshare · 2015

<p>Cluster validation curves for the range 2 ≤ k ≤ 10. The <i>distortion</i> curve (grey line) has a sharp rise in correspondence of the optimal number of clusters k = 4. The validation curve (black line) is produced by taking the difference between consecutive values of the <i>distortion</i> curve, and exhibits a “jump” in correspondence of k = 4.</p>

Cited by 0Open access

Bayesian cluster validation

Hoyt Koepke · Open Collections · 2008

We propose a novel framework based on Bayesian principles for validating clusterings and present efficient algorithms for use with centroid or exemplar based clustering solutions. Our framework treats the data as fixed and introduces perturbations into the clustering procedure. In our algorithms, we scale the distances between points by a random variable whose distribution is tuned against a baseline null dataset. The random variable is integrated out, yielding a soft assignment matrix that gives the behavior under perturbation of the points relative to each of the clusters. From this soft assignment matrix, we are able to visualize inter-cluster behavior, rank clusters, and give a scalar index of the the clustering stability. In a large test on synthetic data, our method matches or outperforms other leading methods at predicting the correct number of clusters. We also present a theoretical analysis of our approach, which suggests that it is useful for high dimensional data.

Cited by 3Open access

Cluster Validation with Gener

James C. Bezdek · 1995

Some generalizations of Dunn's index for validation of crisp clusters are discussed. Numerical examples suggest that this index, which uses the standard measures for inter-set distance and set diameter can be improved by using differentfunctions for either or both of these operators.

Cited by 0

clv: Cluster Validation Techniques

Lukasz Nieweglowski · 2007

Contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in "cluster" package as well as user defined clustering algorithms.

Cited by 9Open access

Cluster validation: An integrative method for cluster analysis

Mahesh Visvanathan, B Srinivas Adagarla, H Lushington Gerald et al. · 2009

Clustering is a widely used to discover underlying patterns and groups in data and there is a need to validate the quality of clusters generated by the numerous clustering algorithms in use. The need for cluster validitation arises from the fundamental definition of unsupervised learning. As clustering is an unsupervised learning process, the prediction of correct number of clusters is a hurdle which can be cleared by using cluster validity indices to assess the quality of the clusters. We have developed a tool for cluster validation as a part of GOAPhAR, a web based tool that integrates from disparate sources, information regarding gene annotations, protein annotations, identifiers associated with probe sets, functional pathways, protein interactions, gene Ontology and publicly available microarray datasets. Our cluster validity tool calculates three indices to indicate clustering quality viz. the Silhouette, Dunn's and Davies-Bouldin indices and outputs them to the user. The values of these indices can be used to judge the quality of clustering and to optimize the process of selecting an appropriate clustering algorithm and number of clusters.

Cited by 13

Computational Cost Reduction and Validation of Cluster-Cluster Aggregation Model

Kiminori Ono, Yoshiya Matsukawa, Aki Watanabe et al. · Journal of the Society of Powder Technology Japan · 2015

This study introduces a new collision detection method obtained by modifying the grid partitioning method, which employs spatial- and cell-partitioning, into an aggregate mean free path-cluster-cluster aggregation （AMP-CCA） model. This modification allows the AMP-CCA model to calculate the three-dimensional aggregate morphology and particle size distributions （PSDs） with computational efficiency. As compared with the previous model, the new model successfully calculates the morphology in 15％ of the computational time. The calculated PSDs for a coalesced spherical particle aggregate, as calculated by the AMP-CCA model, are in reasonable agreement with the results of the sectional model regardless of concentration. The morphology calculated by the AMP-CCA model is in good agreement with previous experimental and numerical results. The AMP-CCA model, employing direct Monte Carlo simulation, serves as a useful tool to calculate the aggregate morphology and PSDs with reasonable accuracy.

Cited by 0Open access

Improving cluster availability using workstation validation

Taliver Heath, Richard P. Martin, Thu D. Nguyen · 2002

We demonstrate a framework for improving the availability of cluster based Internet services. Our approach models Internet services as a collection of interconnected components, each possessing well defined interfaces and failure semantics. Such a decomposition allows designers to engineer high availability based on an understanding of the interconnections and isolated fault behavior of each component, as opposed to ad-hoc methods. In this work, we focus on using the entire commodity workstation as a component because it possesses natural, fault-isolated interfaces. We define a failure event as a reboot because not only is a workstation unavailable during a reboot, but also because reboots are symptomatic of a larger class of failures, such as configuration and operator errors. Our observations of 3 distinct clusters show that the time between reboots is best modeled by a Weibull distribution with shape parameters of less than 1, implying that a workstation becomes more reliable the longer it has been operating. Leveraging this observed property, we design an allocation strategy which withholds recently rebooted workstations from active service, validating their stability before allowing them to return to service. We show via simulation that this policy leads to a 70-30 rule-of-thumb: For a constant utilization, approximately 70% of the workstation failures can be masked from end clients with 30% extra capacity added to the cluster, provided reboots are not strongly correlated. We also found our technique is most sensitive to the burstiness of reboots as opposed to absolute lengths of workstation uptimes.

Cited by 112

Cluster Catalog Validation with Multiwavelength Data

E. K. Steelberg, T. Jeltema, Jackson H. O’Donnell et al. · Research Notes of the AAS · 2025

Abstract The Legacy Survey of Space and Time will provide a ground-breaking data set for cosmology, but to achieve the precision needed, the data, data reduction, and algorithms measuring the cosmological data vectors must be thoroughly validated and calibrated. In this note, we focus on clusters of galaxies and present a set of validation tests for optical cluster finding algorithms through comparison to X-ray and Sunyaev–Zel’dovich effect data. As an example, we apply our pipeline to compare the performance of the red-sequence Matched filter Probabilistic Percolation and Wavelet Z Photometric cluster finding algorithms on Dark Energy Survey data. We investigate metrics of completeness, centering, and scaling relations. We find more than 88% of South Pole Telescope clusters with both algorithms, and 8% or fewer of the clusters are found to be miscentered. The intrinsic scatter in both samples is low compared to X-ray temperature.

Cited by 0Open access

Automotive instrument cluster screen content validation

Sergiu Nimară, Dorina Bianca Popa, Răzvan Bogdan · 2017

The automotive industry has exhibited a tremendous development and improvement trend in the last decade. Todays' automobile manufacturers equip their state-of-the-art vehicles with complex LCD Instrument Clusters that provide essential information to the driver. A guideline manual that includes all the features of the car must be provided to the customer. In order to depict all the features of the Instrument Cluster, screenshots of all possible screens of an Instrument Cluster should be grabbed, in all possible languages. This paper describes a novel solution to provide this kind of screenshots to the car manufacturers, called Image Grabber application, using a DSLR digital camera, a CAN case and a C# tool. The proposed solution has no original equipment manufacturer limitations, as it can be used on all types of clusters and provides an implementation cost decrease of up to 10 times, when compared with a similar hardware grabber system.

Cited by 8

GO-based Gene Expression Cluster Validation

Thanh Le · 2015

Fuzzy C-Means (FCM) algorithm has been widely used in cluster analysis of gene expression data. It can converge rapidly and provide more information regarding relationships within the data thanks to the usage of fuzzy sets to represent the degrees of cluster membership for every data point. However, FCM has a shortcoming in that it requires a priori specification of cluster number and cluster validation. Most cluster validity indices are based on the data themselves and may not be applicable for gene expression data. In this paper, we propose a Bayesian method using Gene Ontology (GO) annotations for gene expression cluster validation. We show that our method outperforms popular validity indices on gene expression datasets.

Cited by 1

Cluster validation criteria for image segmentation

Jong-Kae Fwu, Petar M. Djurić · 2002

In this paper cluster validation criteria for piecewise constant image segmentation are proposed. All the criteria are based on the maximum a posteriori (MAP) principle and derived and implemented by four different, but related approaches. They are obtained by using Taylor expansions and three of them are derived by Bayesian predictive densities. The third and fourth criteria are implemented by the bootstrap technique, and their evaluations are, therefore, computationally more intensive than the evaluations of the first two. The proposed rules are compared by computer simulations with the widely used Akaike's information criterion (AIC) and the minimum description length (MDL) criteria.

Cited by 0

A Comparative Approach to Cluster Validation

Zeev Volkovich, Renata Avros, Mati Golani · 2009

The estimation of the appropriate number of clusters is a known problem in cluster anal-ysis, that affects the clusters stability. Given a stable clustering result, it shows that re-clustering matched pairs of samples taken from the same cluster, would result with sim-ilar results. Implementation of this approach employs own technique intended to construct repeated clustering for the same data. A solutions likeness is measured by means of the total misclassification rate among the sample unit, and each sample. Another employed characteristic is the adjusted Rand Index which is calculated between the unit source clustering, and the one induced by the individual samples partitions. Drawbacks of clus-tering algorithms together with the data inconsistency can seriously increase the model uncertainty. Thus, the inference according to the “true ” number of clusters is based on a sufficiently large amount of information, and the process is repeated multiple times for constructing empirical distributions of the disagree rate and the adjusted Rand coefficient. The “true ” number of clusters corresponds to the rate of empirical distribution having a minimal right tail and the index distribution having a minimal left tail. Numerical experiments demonstrate high ability of the proposed methodology.

Cited by 0

Validation of Cluster Bomb Mathematical Model

Maciej Lasek, Krzysztof Sibilski, Andrzej Żyluk · AIAA Atmospheric Flight Mechanics Conference and Exhibit · 2005

*† ‡ This work consists of the concepts and some results of solutions for aircraft – external store configuration aerodynamic interference. The main emphasis is placed on practical, cost-effective engineering solution of the complex problem with reasonable computational efficiency allowing the code to run on PC computers. Prediction of external stores separation trajectories is an important task in the aerodynamic design area having the objective to define the operational, release envelopes. To attain this purpose, a technique based on the Non-linear Panel Method with unsteady free wake formulation has been developed. The comparison with the flight test as well as wind tunnel investigations has proved the reliability of this methodology.

Cited by 1