• clue

  • Referenced in 14 articles [sw09497]
  • CLUE for CLUster Ensembles. Cluster ensembles are collections of individual solutions to a given clustering ... computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions ... measuring proximity and obtaining consensus and ”secondary” clusterings...
  • iRSpot-EL

  • Referenced in 28 articles [sw24776]
  • auto-cross covariance into an ensemble classifier of clustering approach. Five-fold cross tests...
  • LCE

  • Referenced in 5 articles [sw25277]
  • link-based cluster ensemble method for improved gene expression data analysis. MOTIVATION ... another, this is often sub-optimal. Cluster ensemble research solves this problem by automatically combining ... data clustering. RESULTS: The link-based cluster ensemble (LCE) method, presented here, implements these ideas ... usually outperforms the existing cluster ensemble algorithms in individual tests and, overall, is clearly class...
  • LinkCluE

  • Referenced in 2 articles [sw15839]
  • MATLAB Package for Link-Based Cluster Ensembles. Cluster ensembles have emerged as a powerful meta ... robustness by aggregating several input data clusterings. In particular, link-based similarity methods have recently ... LinkCluE, that implements the link-based cluster ensemble framework. A variety of functional methods...
  • RNACluster

  • Referenced in 2 articles [sw23124]
  • effective cluster approach for the ensemble clustering using a minimum spanning tree (MST) based algorithm ... structure prediction based on the clustering of structure ensemble...
  • LibD3C

  • Referenced in 4 articles [sw21627]
  • LibD3C: ensemble classifiers with a clustering and dynamic selection strategy. Selective ensemble is a learning ... hybrid model of ensemble pruning that is based on k-means clustering and the framework...
  • ArrayMining

  • Referenced in 2 articles [sw25183]
  • data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods ... wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods ... combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration ... fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from...
  • OTclust

  • Referenced in 1 article [sw35615]
  • Cluster Analysis. Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures...
  • OpenEnsembles

  • Referenced in 0 articles [sw26686]
  • OpenEnsembles: a Python resource for ensemble clustering. In this paper we introduce OpenEnsembles, a Python ... toolkit for performing and analyzing ensemble clustering. Ensemble clustering is the process of creating many ... clustering solutions for a given dataset and utilizing the relationships observed across the ensemble ... clustering data, visualizing individual clustering solutions, visualizing and finishing the ensemble, and calculating validation metrics...
  • FastXML

  • Referenced in 5 articles [sw30152]
  • label space. FastXML is an efficient tree ensemble based extreme classifier that can scale ... desktop/small cluster and can make predictions in milliseconds per test point. Tree ensembles generally require...
  • KUBO

  • Referenced in 2 articles [sw17872]
  • conductance of a statistical ensemble of two-dimensional clusters of the square lattice ... quite general. The shape of the cluster is rectangular with ideal leads attached to opposite ... system of given parameters or a statistical ensemble of conductances measured for different disorder realizations...
  • RcmdrPlugin.FuzzyClust

  • Referenced in 0 articles [sw16677]
  • result, this package provide soft voting cluster ensemble function. Visualization of result are provided...
  • MDMC2

  • Referenced in 1 article [sw17668]
  • Carlo simulations of multiply charged clusters in the NVTNVT ensemble (Bonhommeau and Gaigeot, 2013). Both ... mesoscopic coarse-grained simplified representation of the clusters (or droplets): these clusters are composed...
  • PLANET

  • Referenced in 5 articles [sw15434]
  • classification and regression trees, as well as ensembles of such models. We discuss the benefits ... challenges of using a MapReduce compute cluster for tree learning, and demonstrate the scalability...
  • kombine

  • Referenced in 1 article [sw26501]
  • sampler. kombine is an ensemble sampler that uses a clustered kernel-density-estimate proposal density...
  • QMEAN

  • Referenced in 7 articles [sw17377]
  • geometrical analysis of single models, and the clustering-based scoring function QMEANclust which calculates ... comparison of the models from the ensemble provided by the user. The web server performs...
  • MLC Toolbox

  • Referenced in 1 article [sw28399]
  • space dimension reduction, sample clustering, label space dimension reduction and ensemble...
  • RNAG

  • Referenced in 2 articles [sw17129]
  • ensemble centroids, and at least 11 families had at least two well-separated clusters ... variation among structures within an ensemble. Availability: The Perl implementation of the RNAG algorithm...
  • Hyperbolic graph generator

  • Referenced in 5 articles [sw19944]
  • properties like heterogeneous degree distributions and strong clustering. Recent research on network geometry has shown ... random graphs from other well-known graph ensembles, such as the soft configuration model, random...
  • SWIFT

  • Referenced in 2 articles [sw26872]
  • ORFs, by means of the k-means clustering of the candidate ORFs. The search ... sequence, without sifting through an ensemble of previously determined ORFs. Thus, an exhaustive examination...