Data
London-smart-meter-data-preprocessed-for-clustering

London-smart-meter-data-preprocessed-for-clustering

in_preparation ARFF Public Domain (CC0) Visibility: public Uploaded 15-03-2018 by Peter Laurinec
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  • clustering electricity_consumption forecasting smart_meter time_series
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Help us complete this description Edit
Original data were acquired here: https://www.kaggle.com/jeanmidev/smart-meters-in-london Dataset contains smart meter data from London. Each row represents one consumer (together 5066 consumers). Dates are from 1.5.2013 - 30.9.2019 so together 7344 columns (period is 48).

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V546numeric857 unique values
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V1314numeric773 unique values
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V2338numeric877 unique values
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V3106numeric801 unique values
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V4130numeric560 unique values
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V4642numeric790 unique values
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0 missing
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V6434numeric546 unique values
0 missing
V6690numeric758 unique values
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V6946numeric957 unique values
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V7202numeric571 unique values
0 missing
V35numeric835 unique values
0 missing
V291numeric484 unique values
0 missing
V547numeric886 unique values
0 missing
V803numeric935 unique values
0 missing
V1059numeric520 unique values
0 missing
V1315numeric763 unique values
0 missing
V1571numeric867 unique values
0 missing
V1827numeric523 unique values
0 missing
V2083numeric764 unique values
0 missing
V2339numeric876 unique values
0 missing
V2595numeric500 unique values
0 missing
V2851numeric766 unique values
0 missing
V3107numeric800 unique values
0 missing
V3363numeric489 unique values
0 missing

62 properties

5066
Number of instances (rows) of the dataset.
7344
Number of attributes (columns) of the dataset.
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.
7344
Number of numeric attributes.
0
Number of nominal attributes.
0.17
Mean of means among attributes of the numeric type.
3.53
First quartile of skewness among attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.18
First quartile of standard deviation of attributes of the numeric type.
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.
1.45
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
26.85
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.
4.68
Mean skewness among attributes of the numeric type.
0.17
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
0.21
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
4.1
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
7.65
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.22
Second quartile (Median) of standard deviation of attributes of the numeric type.
364.84
Maximum kurtosis among attributes of the numeric type.
0.09
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.33
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.
53.55
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
0.21
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
2.33
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
12.56
Maximum skewness among attributes of the numeric type.
0.1
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
5.64
Third quartile of skewness among attributes of the numeric type.
0.35
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
19.47
First quartile of kurtosis among attributes of the numeric type.
0.25
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.14
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
40
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.

9 tasks

0 runs - estimation_procedure: 50 times Clustering
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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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