Data
Peak-Detection-Dataset

Peak-Detection-Dataset

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context I am currently writing a seminar paper about content summarization in Twitter networks. One of the Frameworks I read about uses a peak detection algorithm to cluster tweets by topic-aware peak times. I started wondering if there are any interesting peak detection approaches. Feel free to play around with the data and provide your peak detection algorithms! They don't necessarily have to be machine learning algorithms.

52 features

Unnamed:_0numeric30000 unique values
0 missing
signal_day_0numeric30000 unique values
0 missing
signal_day_1numeric30000 unique values
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signal_day_2numeric30000 unique values
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signal_day_3numeric30000 unique values
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signal_day_4numeric30000 unique values
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signal_day_5numeric30000 unique values
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signal_day_6numeric30000 unique values
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signal_day_7numeric30000 unique values
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signal_day_8numeric30000 unique values
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signal_day_10numeric30000 unique values
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signal_day_11numeric30000 unique values
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signal_day_12numeric30000 unique values
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signal_day_14numeric30000 unique values
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signal_day_15numeric30000 unique values
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signal_day_16numeric30000 unique values
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signal_day_17numeric30000 unique values
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signal_day_18numeric30000 unique values
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signal_day_19numeric30000 unique values
0 missing
signal_day_20numeric30000 unique values
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signal_day_21numeric30000 unique values
0 missing
signal_day_22numeric30000 unique values
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signal_day_23numeric30000 unique values
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signal_day_24numeric30000 unique values
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signal_day_25numeric30000 unique values
0 missing
signal_day_26numeric30000 unique values
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signal_day_27numeric30000 unique values
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signal_day_28numeric30000 unique values
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signal_day_29numeric30000 unique values
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signal_day_30numeric30000 unique values
0 missing
signal_day_31numeric30000 unique values
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signal_day_32numeric30000 unique values
0 missing
signal_day_33numeric30000 unique values
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signal_day_34numeric30000 unique values
0 missing
signal_day_35numeric30000 unique values
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signal_day_36numeric30000 unique values
0 missing
signal_day_37numeric30000 unique values
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signal_day_38numeric30000 unique values
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signal_day_39numeric30000 unique values
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signal_day_40numeric30000 unique values
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signal_day_41numeric30000 unique values
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signal_day_42numeric30000 unique values
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signal_day_43numeric30000 unique values
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signal_day_45numeric30000 unique values
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signal_day_48numeric30000 unique values
0 missing
signal_day_49numeric30000 unique values
0 missing
peaks(TARGET)numeric8 unique values
0 missing

19 properties

30000
Number of instances (rows) of the dataset.
52
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.
52
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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