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UNIX_user_data

UNIX_user_data

active ARFF Publicly available Visibility: public Uploaded 27-09-2014 by Joaquin Vanschoren
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Author: Terran Lane (terran@ecn.purdue.edu) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/UNIX+User+Data) - Date unknown Please cite: This file contains 9 sets of sanitized user data drawn from the command histories of 8 UNIX computer users at Purdue over the course of up to 2 years (USER0 and USER1 were generated by the same person, working on different platforms and different projects). The data is drawn from tcsh(1) history files and has been parsed and sanitized to remove filenames, user names, directory structures, web addresses, host names, and other possibly identifying items. Command names, flags, and shell metacharacters have been preserved. Additionally, SOF and EOF tokens have been inserted at the start and end of shell sessions, respectively. Sessions are concatenated by date order and tokens appear in the order issued within the shell session, but no timestamps are included in this data. For example, the two sessions: cd ~/private/docs ls -laF | more cat foo.txt bar.txt zorch.txt > somewhere exit cd ~/games/ xquake & fg vi scores.txt mailx john_doe@somewhere.com exit would be represented by the token stream SOF cd \<1\> (one "file name" argument) ls -laF | more cat \<3\> (three "file" arguments) \> \<1\> exit EOF SOF cd \<1\> xquake & fg vi \<1\> mailx \<1\> exit EOF This data is made available under conditions of anonymity for the contributing users and may be used for research purposes only. Summaries and research results employing this data may be published, but literal tokens or token sequences from the data may not be published except with express consent of the originators of the data. No portion of this data may be released with or included in a commercial product, nor may any portion of this data be sold or redistributed for profit or as part of of a profit-making endeavor.

3 features

user (target)nominal9 unique values
0 missing
historynumeric2961 unique values
0 missing
sessionstring5964 unique values
0 missing

107 properties

9100
Number of instances (rows) of the dataset.
3
Number of attributes (columns) of the dataset.
9
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.
1
Number of numeric attributes.
1
Number of nominal attributes.
1
Average class difference between consecutive instances.
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
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
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
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
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
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
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
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
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
2.93
Entropy of the target attribute values.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
26.65
Percentage of instances belonging to the most frequent class.
2425
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
0.93
Maximum kurtosis among attributes of the numeric type.
885.17
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
9
The maximum number of distinct values among attributes of the nominal type.
1.24
Maximum skewness among attributes of the numeric type.
790.05
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
0.93
Mean kurtosis among attributes of the numeric type.
885.17
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
9
Average number of distinct values among the attributes of the nominal type.
1.24
Mean skewness among attributes of the numeric type.
790.05
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
0.93
Minimum kurtosis among attributes of the numeric type.
885.17
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
9
The minimal number of distinct values among attributes of the nominal type.
1.24
Minimum skewness among attributes of the numeric type.
790.05
Minimum standard deviation of attributes of the numeric type.
5.32
Percentage of instances belonging to the least frequent class.
484
Number of instances belonging to the least frequent class.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
33.33
Percentage of numeric attributes.
33.33
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.93
First quartile of kurtosis among attributes of the numeric type.
885.17
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
1.24
First quartile of skewness among attributes of the numeric type.
790.05
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.93
Second quartile (Median) of kurtosis among attributes of the numeric type.
885.17
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1.24
Second quartile (Median) of skewness among attributes of the numeric type.
790.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.93
Third quartile of kurtosis among attributes of the numeric type.
885.17
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
1.24
Third quartile of skewness among attributes of the numeric type.
790.05
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

10 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: user
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: user
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: user
0 runs - estimation_procedure: Leave one out - target_feature: user
0 runs - estimation_procedure: 10% Holdout set - target_feature: user
0 runs - estimation_procedure: 33% Holdout set - target_feature: user
0 runs - estimation_procedure: Test on Training Data - target_feature: user
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: user
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: user
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: user
Define a new task