文件名称:COMPACT.2.0
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Matlab GUI工具,支持一种简单直观的方法来比较聚类方法。-Outline
COMPACT is a GUI Matlab tool that enables an easy and intuitive way to compare some clustering methods.
COMPACT is a five-step wizard that envelops some basic Matlab clustering methods and introduces the Quantum clustering algorithm that was originally proposed by Prof. David Horn and Assaf Gottlieb (see The Method of Quantum Clustering, for more details). COMPACT provides a flexible and customizable interface for clustering data with high dimensionality.
COMPACT allows both textual and graphical display for the clustering results.
COMPACT is a GUI Matlab tool that enables an easy and intuitive way to compare some clustering methods.
COMPACT is a five-step wizard that envelops some basic Matlab clustering methods and introduces the Quantum clustering algorithm that was originally proposed by Prof. David Horn and Assaf Gottlieb (see The Method of Quantum Clustering, for more details). COMPACT provides a flexible and customizable interface for clustering data with high dimensionality.
COMPACT allows both textual and graphical display for the clustering results.
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下载文件列表
clusterResultsDlg.fig
clustersTextualResults.fig
clustersTool.fig
clustersTool2.fig
clustersTool3.fig
clustersToolMethods.fig
clustersToolMethodsNoBIC.fig
clustersToolResults.fig
fileBrowser.fig
fuzzyCMeansDlg.fig
guiClusters.fig
guiSimulatePE.fig
lb.fig
matrixShapeDlg.fig
numOfClassesDlg.fig
parallelCoordsFig.fig
paramsForCNNDlg.fig
paramsForQCDlg.fig
paramsForSensitiveQCDlg.fig
paramsForSVCDlg.fig
secondOrderClusteringDlg.fig
select3DimsFig.fig
test1.fig
calcJaccard.m
calculateJaccardByReference.m
clr.m
clusterResultsDlg.m
clustersTextualResults.m
clustersTool.m
clustersTool2.m
clustersTool3.m
clustersToolMethods.m
clustersToolMethodsNoBIC.m
clustersToolResults.m
cnnClustering.m
compact.m
compareMethodsTool.m
establishPairsMatrix.m
estimateNumberOfSignificantDims.m
fileBrowser.m
fillDataInput.m
findMinima.m
findNumClust.m
fineCluster.m
fuzzyCMeansDlg.m
graddesc.m
graddesc.tryascent.m
groupAfterGradientDescent.m
guiClusters.m
guiSimulatePE.m
guiTruncateInput.m
guiTruncateInputWithSelectedDims.m
lb.m
lmax.m
lmax_pw.m
lmin.m
lmin_pw.m
matrixShapeDlg.m
myClustMeasure.m
myShowQc.m
numOfClassesDlg.m
parallelCoordsFig.m
paramsForCNNDlg.m
paramsForQCDlg.m
paramsForSensitiveQCDlg.m
paramsForSVCDlg.m
pathdef.m
plotClassification.m
plotClassificationInParallelCoordinates.m
plotClusters.m
plotClustersInColors.m
plotPEJ.m
plotRecTree.m
plotTruncateData.m
pureQC.m
qc.m
qcClustering.m
qcClustering.tryascent.m
qui.m
readInputFile.m
recCompact.m
referenceClustering.m
relabelClassificationFromReference.m
reorderData.m
script.m
secondOrderClustering.m
secondOrderClusteringDlg.m
select3DimsFig.m
show_qc.m
silentCompareMethodsTool.m
test1.m
treeplot.m
truncateInput.m
clustersTextualResults.fig
clustersTool.fig
clustersTool2.fig
clustersTool3.fig
clustersToolMethods.fig
clustersToolMethodsNoBIC.fig
clustersToolResults.fig
fileBrowser.fig
fuzzyCMeansDlg.fig
guiClusters.fig
guiSimulatePE.fig
lb.fig
matrixShapeDlg.fig
numOfClassesDlg.fig
parallelCoordsFig.fig
paramsForCNNDlg.fig
paramsForQCDlg.fig
paramsForSensitiveQCDlg.fig
paramsForSVCDlg.fig
secondOrderClusteringDlg.fig
select3DimsFig.fig
test1.fig
calcJaccard.m
calculateJaccardByReference.m
clr.m
clusterResultsDlg.m
clustersTextualResults.m
clustersTool.m
clustersTool2.m
clustersTool3.m
clustersToolMethods.m
clustersToolMethodsNoBIC.m
clustersToolResults.m
cnnClustering.m
compact.m
compareMethodsTool.m
establishPairsMatrix.m
estimateNumberOfSignificantDims.m
fileBrowser.m
fillDataInput.m
findMinima.m
findNumClust.m
fineCluster.m
fuzzyCMeansDlg.m
graddesc.m
graddesc.tryascent.m
groupAfterGradientDescent.m
guiClusters.m
guiSimulatePE.m
guiTruncateInput.m
guiTruncateInputWithSelectedDims.m
lb.m
lmax.m
lmax_pw.m
lmin.m
lmin_pw.m
matrixShapeDlg.m
myClustMeasure.m
myShowQc.m
numOfClassesDlg.m
parallelCoordsFig.m
paramsForCNNDlg.m
paramsForQCDlg.m
paramsForSensitiveQCDlg.m
paramsForSVCDlg.m
pathdef.m
plotClassification.m
plotClassificationInParallelCoordinates.m
plotClusters.m
plotClustersInColors.m
plotPEJ.m
plotRecTree.m
plotTruncateData.m
pureQC.m
qc.m
qcClustering.m
qcClustering.tryascent.m
qui.m
readInputFile.m
recCompact.m
referenceClustering.m
relabelClassificationFromReference.m
reorderData.m
script.m
secondOrderClustering.m
secondOrderClusteringDlg.m
select3DimsFig.m
show_qc.m
silentCompareMethodsTool.m
test1.m
treeplot.m
truncateInput.m
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