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wind_correlations

wind_correlations

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Author: Source: Unknown - Date unknown Please cite: These data are estimated correlations between daily 3 p.m. wind measurements during September and October 1997 for a network of 45 stations in the Sydney region. The first column below gives a list of station latitudes, the second gives a list of station longitudes, and the remaining 45 columns give the 45 x 45 spatial correlation matrix of the station measurements. Further details about the data are contained in the following technical report: Nott and Dunsmuir (1998) ``Analysis of Spatial Covariance Structure from Monitoring Data,'' Technical Report, Department of Statistics, University of New South Wales. Email djn@maths.unsw.edu.au with any questions or to obtain a copy of the latest version of the above report. Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

47 features

station_34numeric45 unique values
0 missing
station_23numeric40 unique values
0 missing
station_24numeric43 unique values
0 missing
station_25numeric43 unique values
0 missing
station_26numeric44 unique values
0 missing
station_27numeric44 unique values
0 missing
station_28numeric44 unique values
0 missing
station_29numeric45 unique values
0 missing
station_30numeric45 unique values
0 missing
station_31numeric44 unique values
0 missing
station_32numeric44 unique values
0 missing
station_33numeric44 unique values
0 missing
station_22numeric44 unique values
0 missing
station_35numeric44 unique values
0 missing
station_36numeric44 unique values
0 missing
station_37numeric44 unique values
0 missing
station_38numeric43 unique values
0 missing
station_39numeric45 unique values
0 missing
station_40numeric43 unique values
0 missing
station_41numeric44 unique values
0 missing
station_42numeric42 unique values
0 missing
station_43numeric41 unique values
0 missing
station_44numeric45 unique values
0 missing
station_45numeric44 unique values
0 missing
station_11numeric44 unique values
0 missing
longitudenumeric44 unique values
0 missing
station_1numeric44 unique values
0 missing
station_2numeric45 unique values
0 missing
station_3numeric44 unique values
0 missing
station_4numeric44 unique values
0 missing
station_5numeric44 unique values
0 missing
station_6numeric43 unique values
0 missing
station_7numeric42 unique values
0 missing
station_8numeric41 unique values
0 missing
station_9numeric43 unique values
0 missing
station_10numeric44 unique values
0 missing
latitudenumeric43 unique values
0 missing
station_12numeric45 unique values
0 missing
station_13numeric42 unique values
0 missing
station_14numeric43 unique values
0 missing
station_15numeric43 unique values
0 missing
station_16numeric43 unique values
0 missing
station_17numeric45 unique values
0 missing
station_18numeric45 unique values
0 missing
station_19numeric45 unique values
0 missing
station_20numeric45 unique values
0 missing
station_21numeric44 unique values
0 missing
Show all 47 features

107 properties

NumberOfInstances
45
Number of instances (rows) of the dataset.
NumberOfFeatures
47
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
47
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
AutoCorrelation
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
1.04
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
12.99
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
150.88
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
2.62
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.53
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1.12
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
2.94
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.17
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.21
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.75
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-33.86
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.17
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.13
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.02
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.41
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.17
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.16
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.47
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.48
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.18
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.71
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.55
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.3
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.23
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

Clustering on wind_correlations
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Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
Clustering on wind_correlations
0 runs - estimation_procedure: 50 times Clustering
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