Data
synthetic_control

synthetic_control

in_preparation ARFF Publicly available Visibility: public Uploaded 06-11-2018 by Giuseppe Casalicchio
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Author: Dr Robert Alcock (rob@skyblue.csd.auth.gr) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series) - 1999 Please cite: Synthetic Control Chart Time Series This data consists of synthetically generated control charts. This dataset contains 600 examples of control charts synthetically generated by the process in Alcock and Manolopoulos (1999). There are six different classes of control charts: 1. Normal 2. Cyclic 3. Increasing trend 4. Decreasing trend 5. Upward shift 6. Downward shift Past Usage Alcock R.J. and Manolopoulos Y. Time-Series Similarity Queries Employing a Feature-Based Approach. 7th Hellenic Conference on Informatics. August 27-29. Ioannina,Greece 1999. References and Further Information D.T. Pham and A.B. Chan "Control Chart Pattern Recognition using a New Type of Self Organizing Neural Network" Proc. Instn, Mech, Engrs. Vol 212, No 1, pp 115-127 1998. References 1. http://skyblue.csd.auth.gr/~rob/ 2. mailto:rob@skyblue.csd.auth.gr 3. http://kdd.ics.uci.edu/ 4. http://www.ics.uci.edu/ 5. http://www.uci.edu/

61 features

class (target)nominal6 unique values
0 missing
index (row identifier)nominal600 unique values
0 missing
col_1numeric597 unique values
0 missing
col_2numeric600 unique values
0 missing
col_3numeric599 unique values
0 missing
col_4numeric598 unique values
0 missing
col_5numeric597 unique values
0 missing
col_6numeric596 unique values
0 missing
col_7numeric596 unique values
0 missing
col_8numeric598 unique values
0 missing
col_9numeric599 unique values
0 missing
col_10numeric596 unique values
0 missing
col_11numeric599 unique values
0 missing
col_12numeric600 unique values
0 missing
col_13numeric600 unique values
0 missing
col_14numeric597 unique values
0 missing
col_15numeric596 unique values
0 missing
col_16numeric599 unique values
0 missing
col_17numeric599 unique values
0 missing
col_18numeric600 unique values
0 missing
col_19numeric597 unique values
0 missing
col_20numeric598 unique values
0 missing
col_21numeric595 unique values
0 missing
col_22numeric596 unique values
0 missing
col_23numeric597 unique values
0 missing
col_24numeric599 unique values
0 missing
col_25numeric598 unique values
0 missing
col_26numeric600 unique values
0 missing
col_27numeric597 unique values
0 missing
col_28numeric599 unique values
0 missing
col_29numeric599 unique values
0 missing
col_30numeric597 unique values
0 missing
col_31numeric600 unique values
0 missing
col_32numeric600 unique values
0 missing
col_33numeric599 unique values
0 missing
col_34numeric600 unique values
0 missing
col_35numeric600 unique values
0 missing
col_36numeric599 unique values
0 missing
col_37numeric600 unique values
0 missing
col_38numeric599 unique values
0 missing
col_39numeric600 unique values
0 missing
col_40numeric596 unique values
0 missing
col_41numeric600 unique values
0 missing
col_42numeric600 unique values
0 missing
col_43numeric600 unique values
0 missing
col_44numeric599 unique values
0 missing
col_45numeric600 unique values
0 missing
col_46numeric600 unique values
0 missing
col_47numeric598 unique values
0 missing
col_48numeric600 unique values
0 missing
col_49numeric600 unique values
0 missing
col_50numeric600 unique values
0 missing
col_51numeric599 unique values
0 missing
col_52numeric600 unique values
0 missing
col_53numeric600 unique values
0 missing
col_54numeric600 unique values
0 missing
col_55numeric599 unique values
0 missing
col_56numeric599 unique values
0 missing
col_57numeric600 unique values
0 missing
col_58numeric600 unique values
0 missing
col_59numeric599 unique values
0 missing
col_60numeric599 unique values
0 missing

62 properties

600
Number of instances (rows) of the dataset.
61
Number of attributes (columns) of the dataset.
6
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.
60
Number of numeric attributes.
1
Number of nominal attributes.
30.46
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.
98.36
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6
The maximum number of distinct values among attributes of the nominal type.
-0.69
Minimum skewness among attributes of the numeric type.
1.64
Percentage of nominal attributes.
0.08
Third quartile of skewness among attributes of the numeric type.
0.93
Maximum skewness among attributes of the numeric type.
3.5
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
13.53
Third quartile of standard deviation of attributes of the numeric type.
15.6
Maximum standard deviation of attributes of the numeric type.
16.67
Percentage of instances belonging to the least frequent class.
-1.12
First quartile of kurtosis 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.
100
Number of instances belonging to the least frequent class.
29.73
First quartile of means among attributes of the numeric type.
-0.56
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
30.09
Mean of means among attributes of the numeric type.
-0.05
First quartile of skewness among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
5.85
First quartile of standard deviation of attributes of the numeric type.
2.58
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.
0.1
Number of attributes divided by the number of instances.
6
Average number of distinct values among the attributes of the nominal type.
-0.75
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.
0.03
Mean skewness among attributes of the numeric type.
30.09
Second quartile (Median) of means among attributes of the numeric type.
16.67
Percentage of instances belonging to the most frequent class.
9.75
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
100
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.21
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
9.57
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.6
Maximum kurtosis among attributes of the numeric type.
28.15
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.03
Third quartile of kurtosis among attributes of the numeric type.
32.23
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.

3 tasks

0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
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