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1. Objective: We want to detect outliers in bitcoin transactions that may reflect unusual activity.
2. License: Free to use but requires acknowledgement of http://compbio.cs.uic.edu
3. Data Source: http://compbio.cs.uic.edu/data/bitcoin/
4. DataSet Info: We have retained the first 100k records from the original dataset that contained 37.5 million records.
5. Field Meanings:
A. transaction_id: Bitcoin transaction ID. Note that it is not a primary key.
B. user_id_from: ID of user who sent the bitcoins.
C. user_id_to: ID of user who received the bitcoins.
D. date: When the transaction occurred.
E. value: Amount of bitcoins being transferred.
F. isOutlier: 1 (Outlier), 0 (Normal)
6. Parameter Selection:
A. Dashboard Usage: Detect Categorical Outlier
Settings:
1) Search command: | inputlookup bitcoin_transactions.csv | eval from_user = "u".user_id_from | eval to_user = "u".user_id_to
2) Field(s) to analyze: from_user, to_user, value