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Recursive Agglomerative Clustering (RecAgglo) for categorical data

Code and simple experimental setup for the Recursive Agglomerative Clustering (RecAgglo) algorithm introduced in Detecting organized eCommerce fraud using scalable categorical clustering (Marchal S. and Szyller S., ACSAC 2019). This algorithm is initially designed to cluster orders placed on eCommerce websites with the aim to group fraudulent orders together. RecAgglo generates a large number of small clusters and it is best suited for processing data represented by attributes having high cardinality. A detailed presentation of the algorithm is provided in Section 3 (Marchal S. and Szyller S., ACSAC 2019). RecAgglo clustering performance (Impurity + computation time) is assessed and compared to state-of-the-art categorical clustering algorithms in Section 7. Its capabilities for fraud detection are assessed in Section 8 (Marchal S. and Szyller S., ACSAC 2019).

Requirements and setup

Python 3 packages: numpy, scipy, pandas, argparse. Also, you need a C compiler and Cython to compile the cython code. The code was tested with Python versions 3.6 and 3.7.

pip3 install numpy, scipy, pandas, argparse, Cython

Alternatively, you can use the provided requirements.txt file:

pip3 install -r requirements.txt

Compile the cython package asym_linkage.pyx with this command:

python3 build_ext --inplace



This packages consists of 3 main files:

  • main body of the experimental setup
  • containing the implementation of RecAgglo
  • custom argument parsing for the provided experiments
  • and asym_linkage.pyx containing fast implementation and compilation of the asymmetrical linkage function


Additionally, test_data directory contains standard datasets used to assess categorical clustering algorithms (from UCI Repository of Machine Learning Databases -

  • car
  • census
  • mushroom
  • nursery

Running the code

To run the clustering algorithm with default parameters, you need to provide just the input (data) and output file name.

  • input file must be a comma separated value (CSV) file with one element per line
  • output file is also a comma separated value (CSV) file, same shape as input file with one additional column containing the cluster index of each element

To run:

python3 -i test_data/ -o result-mushroom.csv --verbose

If you want to use custom parameters, invoke help to get more info:

python3 -h

usage: [-h] --infile INFILE --outfile OUTFILE [--delta_a INT]
               [--delta_fc INT] [--d_max FLOAT] [--rho_mc FLOAT]
               [--rho_s FLOAT] [--skip_index] [--verbose]

RecAgglo clustering. To run: python3 --infile XYZ --outfile XYZ.
Override default args as necessary.

optional arguments:
  -h, --help            show this help message and exit
  --algorithm INT       clustering algorithm to use: 0=RecAgglo,
                        1=SampleClust, 2=AggloClust
  --weight WEIGHT       List of weight for each attribute [0,0.5,...,2.5]
                        (default weights = 1.)
  --delta_a INT         Threshold for cluster sampling (default = 1000). Must
                        be > 0.
  --delta_fc INT        Threshold for full clustering (default = 1). Must be >
  --d_max FLOAT         Distance threshold to split clusters (default = 0.5).
                        Must be > 0.
  --rho_mc FLOAT        Divider of max cluster according to n samples,
                        max_clust = n/mclust (default = 6.0). Must be > 0.
  --rho_s FLOAT         Multiplier of sqrt(n) for sample size (default = 0.5).
                        Must be > 0.
  --skip_index          Skip first column if it is an index and not a
  --verbose             Verbose printing.

Required input/output file arguments:
  --infile INFILE, -i INFILE
                        CSV file containing input data.
  --outfile OUTFILE, -o OUTFILE
                        CSV output file containing input data + cluster


Recursive Agglomerative Clustering (RecAgglo) for categorical data







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