Supplementary material concerning repeatability

Paper ID	432
Title	HiCS: High Contrast Subspaces for Density-Based Outlier Ranking Data
Authors	Fabian Keller, Emmanuel Müller, Klemens Böhm

Datasets

All datasets used in the following experiments can be downloaded here:

Archive containing all 21 synthetic datasets.
Archive containing all real world datasets after the application of our preprocessing.

Experiments on Synthetic Data

General remarks:

The MinPts setting for the underlying LOF algorithm was always set to 100 for experiments on synthetic data
All subspace outlier mining approaches were limit to the top 100 of the subspace-search result.

Experiment configurations:

In total, 21 synthetic datasets were generated: 3 datasets for each dimensionality in [10, 20, 30, 40, 50, 75, 100].
We performed serveral algorithmic configurations on all these datasets. The algorithm configurations were:

LOF (1 configuration, same MinPts setting as all subspace search approaches)
PCALOF1 (1 configuration) with a dimensionality reduction to 50% of the dataset dimensionality.
PCALOF2 (1 configuration) with a fixed dimensionality reduction to exactly 10 dimensions.
ENCLUS (27 configuration) with the following parameter combinations:

Number of bins per dimension ∈ [5, 7, 10]
ω ∈ [7.0, 8.0, 9.0]
ε ∈ [0.001, 0.01, 0.02]

RIS (9 configuration) with the following parameter combinations:

ε ∈ [0.05, 0.1, 0.2]
MinPts value for core objects ∈ [10, 20, 30]

RANDSUB (1 configuration) using 100 random subspaces.
HiCS (2 configuration): Only the type of statistical test was varied. According to the results of preliminary experiments, a specific search for parameter values is not necessary for HiCS. We were using the default values:

Number of runs = 50
α = 0.1
Candidate cutoff = 400

In summary, this leads to a total number of 21 * 42 = 882 experiments (corresponding to a processing time of about 5 days, mainly due to RIS).

Experiments on Real World Data

The following experiments were performed with each best algorithm configuration from the experiment on synthetic data. We applied a standardized preprocessing procedure to all datasets (rescaling all attributes, removing categorical attributes or attributes that show strong discretization effects). The arff-files included in the downloadable zip-archive include all results from these preprocessing steps. Furthermore we also stored our outlier definition in these files (always in the last attribute, 0 = no outlier, 1 = outlier). The following figures show the ROC plots of all experiments.

Ann-Thyroid:	Arrythmia:
Breast:	Breast (diagnostic):
Diabetes:	Glass:
Ionosphere:	Pendigits:

Resources

The reviewer is expected to agree to confidentiality requirements with respect to non-disclosure of data on this website, as the reviewer does for any paper under review. Usage is limited to repeating and exploring the experimental results of this paper. Until this work has not been published, no other use is allowed, especially not for other publications. This website conveniently documents the experimental setup used in the evaluation described in our manuscript. We will provide additional experimental data, setup, and software, which will be made available when the manuscript is published.

Public access to this website

After publication of this work, we encourage researchers in this area to use the proposed algorithm for their own publications as competitor. Our implementation will then be available for anyone to use. Thus, all algorithms, data sets and parameter setting will be available for the community.

Supplementary material concerning repeatability

Datasets

Experiments on Synthetic Data

Experiments on Real World Data

Ann-Thyroid:

Arrythmia:

Breast:

Breast (diagnostic):

Diabetes:

Glass:

Ionosphere:

Pendigits:

Resources

Public access to this website