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Table 7: Geometric cluster attributes

in unknown title
by unknown authors 2007

Table 4. Performance on 100 geometric problems where clusters are inapplicable.

in Finding crucial subproblems to focus global search
by Susan L. Epstein 2006
"... In PAGE 8: ...28 variables. As Table4 indicates, all three of our applicable methods solved these problems in 20000 steps, and did so faster, despite the preliminary time required to detect crucial subproblems. Purely random problems should not display struc- ture.... ..."
Cited by 1

Table 4. Performance on 100 geometric problems where clusters are inapplicable.

in Finding crucial subproblems to focus global search
by Susan L. Epstein 2006
Cited by 1

TABLE IV HIERARCHICAL GEOMETRIC MEAN BASED ON CLUSTERING RESULTS FROM MACHINE A

in unknown title
by unknown authors

TABLE V HIERARCHICAL GEOMETRIC MEAN BASED ON CLUSTERING RESULTS FROM MACHINE B

in unknown title
by unknown authors

Table 1. Characterization of selected clustering algorithms with respect to geometrical and geometry-related properties [2, 18, 19].

in Automatic Document Categorization: Interpreting the Performance of Clustering Algorithms
by Benno Stein, Sven Meyer Zu Eissen 2003
"... In PAGE 4: ... On the other hand, k-Means is not able to identify clusters that deviate much from a spherical shape. Table1 lists typical geometrical properties of prominent clustering algorithms. 2.... ..."
Cited by 3

Table 2 shows the average test error per cluster, the relative cluster size N=m, and the geometrical property Nd of cluster size N times the average distance d within the cluster. Given the per-cluster statistic, we would clearly prefer the model 4-9-2.

in Robust Model Assessment for Neural Networks
by Stefan M. Rüger, Arnfried Ossen
"... In PAGE 6: ...ents (see Figure 2). Dendrograms 4-7-2 and 4-8-2 display compact clusters. The cluster structure begins to decay in dendrogram 4-9-2, and has completely disappeared in 4-10-2 and the following dendrogram. Table2 : Per-cluster statistic... In PAGE 6: ...election method can, e. g. be applied to Boltzmann machines. In our opinion, if a series of weight vectors does not contain a single cluster of similar solutions, the experiment setup (data set, learning algorithm, or network model) is likely to be inadequate. We conjecture that a purely geometrical approach might be su cient to select adequate models (see Table2 ). De ning a suitable measure for the presence and compactness of clusters might eventually lead to an algorithm that autofocuses to an appropriate network structure.... ..."

Table 4. The geometrical mean percentage of clusterers selected by selective voting and selective weighted-voting under different ensemble sizes. ensemble size percentage of selecting

in Abstract Clusterer Ensemble
by Zhi-hua Zhou, Wei Tang

Table 1. Length of Monte Carlo runs for the four-, five- and six-dimensional Ising models, in millions of sampled configurations (MS). Before taking each sample, one Metropolis sweep and a number GC (also shown) of geometrical cluster steps were executed. In a few cases the values of MS and GC shown here represent a weighted average over runs with a different number of cluster steps.

in High-Dimensional Lattice Gases
by J.R. Heringa, H.W.J. Blöte, E. Luijten
Cited by 1

Table 3: Clusters possibly related to cluster 4 (Level: 1e-0). Clusters are sorted by quality (i.e. the minus log of the geometric average of connections). Note that all clusters belong to the super family of Immunoglobulins

in A map of the protein space - An automatic hierarchical classification of all protein sequences
by Golan Yona, Nathan Linial, Naftali Tishby, Michal Linial
"... In PAGE 6: ... The alignments can be found in the web site. Table3 is an illustrative example. It shows the clusters which are possibly related to cluster 4 (Im- munoglobulin V region), ordered by quality value.... ..."
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