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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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_3numeric30000 unique values
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signal_day_5numeric30000 unique values
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signal_day_8numeric30000 unique values
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signal_day_19numeric30000 unique values
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signal_day_21numeric30000 unique values
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signal_day_22numeric30000 unique values
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signal_day_23numeric30000 unique values
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signal_day_26numeric30000 unique values
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signal_day_27numeric30000 unique values
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signal_day_34numeric30000 unique values
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signal_day_35numeric30000 unique values
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signal_day_38numeric30000 unique values
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signal_day_49numeric30000 unique values
0 missing
peaks(TARGET)numeric8 unique values
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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
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
Average class difference between consecutive instances.
100
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
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.

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