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Shuttle_2percent

Shuttle_2percent

in_preparation ARFF Publicly available Visibility: public Uploaded 22-09-2017 by Minh-Anh Le
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Author: Markus Goldstein Thanks to NASA for allowing UCL to use the shuttle datasets. Source: [original](http://www.madm.eu/downloads) - Date unknown Please cite: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. "The shuttle dataset describes radiator positions in a NASA space shuttle with 9 attributes and was designed for supervised anomaly detection. Besides the normal “radi- ator flow” class, about 20% of the original data describe abnormal situations. To reduce the number of anomalies, we select the class 1 as normal and apply a stratified sampling for the classes 2, 3, 5, 6 and 7 [...]. Training and test set are combined in a single big dataset, which has as a result 46,464 instances with 1.89% anomalies."(cite from Goldstein, Markus, and Seiichi Uchida. "A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data." PloS one 11.4 (2016): e0152173.). This dataset is not the original dataset. The target variable "Target" is relabeled into "Normal" and "Anomaly".

10 features

Target (target)nominal2 unique values
0 missing
V1numeric76 unique values
0 missing
V2numeric202 unique values
0 missing
V3numeric48 unique values
0 missing
V4numeric126 unique values
0 missing
V5numeric54 unique values
0 missing
V6numeric281 unique values
0 missing
V7numeric86 unique values
0 missing
V8numeric118 unique values
0 missing
V9numeric75 unique values
0 missing

62 properties

49097
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
9
Number of numeric attributes.
1
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
13.73
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.1
Mean skewness among attributes of the numeric type.
36.87
Second quartile (Median) of means among attributes of the numeric type.
92.85
Percentage of instances belonging to the most frequent class.
48.9
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
45586
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.29
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
1.11
Minimum kurtosis among attributes of the numeric type.
10
Percentage of binary attributes.
20.57
Second quartile (Median) of standard deviation of attributes of the numeric type.
7587.46
Maximum kurtosis among attributes of the numeric type.
-0.06
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
85.12
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
4346.98
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
90
Percentage of numeric attributes.
47.61
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
-20.08
Minimum skewness among attributes of the numeric type.
10
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
26.83
Maximum skewness among attributes of the numeric type.
8.88
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
4.46
Third quartile of skewness among attributes of the numeric type.
218.32
Maximum standard deviation of attributes of the numeric type.
7.15
Percentage of instances belonging to the least frequent class.
4.52
First quartile of kurtosis among attributes of the numeric type.
61.13
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
3511
Number of instances belonging to the least frequent class.
1.19
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
1814.63
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
29.78
Mean of means among attributes of the numeric type.
-0.9
First quartile of skewness among attributes of the numeric type.
0.87
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
13.16
First quartile of standard deviation of attributes of the numeric type.
0.37
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.

8 tasks

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