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I am relativly new to the field of data mining and want to make a anomaly detection on transactional retail data. I want to use a simple anomaly detection (kmeans at the moment) for finding suspicious transactions with operators.

For example I want to use the following attributes:

  • Operator
  • Sales-Sum
  • Discount-Sum
  • Counter of his transactions
  • Counter of his discount transactions

Now I ask myself:

  1. Is it possible to detect outliers/anomalies with this kind of comparison (Sales / Discount Transactions)
  2. Is the K-Means Anomaly Detection the right algorithm for this kind of problem?
  3. How does the algorithm deal with relationships?
  4. Does it make sense to use this attributes?
  5. Do you think a classification task would work out better? Transactional data is very different.
  6. Do you have other suggestions regarding attributes, dimensions, algorithms?
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