Data
Aloi

Aloi

in_preparation ARFF Publicly available Visibility: public Uploaded 22-09-2017 by Minh-Anh Le
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Author: Markus Goldstein E. Schubert","R. Wojdanowski","A. Zimek","H.-P. Kriegel","On Evaluation of Outlier Rankings and Outlier Scores","In Proceedings of the 12th SIAM International Conference on Data Mining (SDM)","Anaheim","CA","2012. Source: [original](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF) - Date unknown Please cite: "J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, The Amsterdam library of object images, Int. J. Comput. Vision, 61(1), 103-112, January, 2005." "The aloi dataset is derived from the “Amsterdam Library of Object Images” collection (see citation request). The original dataset contains about 110 images of 1000 small objects taken under different light conditions and viewing angles. From the original images, a 27 dimensional feature vector was extracted using HSB color histograms. Some objects were chosen as anomalies and the data was down-sampled such that the resulting dataset contains 50,000 instances including 3.02% 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 from the provided URL. The target variable is renamed into "Target" and relabeled into "Normal" and "Anomaly".

28 features

Target (target)nominal2 unique values
0 missing
X0.8728117766203703numeric39004 unique values
0 missing
X4.521122685185185E.6numeric6027 unique values
0 missing
X0.0numeric1103 unique values
0 missing
X3.616898148148148E.5numeric5337 unique values
0 missing
X0.0.1numeric4592 unique values
0 missing
X0.0.2numeric2396 unique values
0 missing
X0.0.3numeric551 unique values
0 missing
X0.0.4numeric744 unique values
0 missing
X0.0.5numeric889 unique values
0 missing
X0.05032687717013889numeric19960 unique values
0 missing
X4.521122685185185E.6.1numeric1914 unique values
0 missing
X0.0.6numeric303 unique values
0 missing
X0.005631058304398148numeric14611 unique values
0 missing
X0.004163953993055556numeric13334 unique values
0 missing
X0.0.7numeric2910 unique values
0 missing
X2.2605613425925925E.6numeric1873 unique values
0 missing
X2.0345052083333332E.5numeric2435 unique values
0 missing
X0.0.8numeric3230 unique values
0 missing
X0.01421214916087963numeric9045 unique values
0 missing
X1.0398582175925926E.4numeric1107 unique values
0 missing
X0.0.9numeric101 unique values
0 missing
X0.025490089699074073numeric12626 unique values
0 missing
X0.004937065972222222numeric8464 unique values
0 missing
X1.1302806712962962E.5numeric1085 unique values
0 missing
X5.425347222222222E.5numeric8950 unique values
0 missing
X0.006804289641203704numeric13233 unique values
0 missing
X0.015385380497685185numeric15994 unique values
0 missing

62 properties

49999
Number of instances (rows) of the dataset.
28
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.
27
Number of numeric attributes.
1
Number of nominal attributes.
4.51
First quartile of skewness among attributes of the numeric type.
0.04
Mean of means among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.2
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.
234.41
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0
Second quartile (Median) of means 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.
20.67
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
96.99
Percentage of instances belonging to the most frequent class.
0.01
Mean standard deviation of attributes of the numeric type.
13.31
Second quartile (Median) of skewness among attributes of the numeric type.
48492
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
3.57
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
1.11
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
30995.61
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
661.77
Third quartile of kurtosis among attributes of the numeric type.
0.9
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
96.43
Percentage of numeric attributes.
0.01
Third quartile of means 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.
3.57
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-1.14
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
21.72
Third quartile of skewness among attributes of the numeric type.
160.05
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
31.51
First quartile of kurtosis among attributes of the numeric type.
0.02
Third quartile of standard deviation of attributes of the numeric type.
0.08
Maximum standard deviation of attributes of the numeric type.
3.01
Percentage of instances belonging to the least frequent class.
1507
Number of instances belonging to the least frequent class.
0
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.
Average entropy of the attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1768.08
Mean kurtosis among attributes of the numeric type.

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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