Implementation CMI
Title | CMI: An Information-Theoretic Contrast Measure for Enhancing Subspace Cluster and Outlier Detection
Authors | Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Fabian Keller, Klemens Böhm
This page provides information on how to run the implementation of our CMI algorithm.
In order to execute CMI you can use the following command line structure:
Parameter | Meaning
-FILE_INPUT | name of input file
-FILE_SUB_OUTPUT | name of output file for subspaces
-FILE_OSCORES_OUTPUT | name of output file for outlier scores
-NUM_ROWS | number of records
-NUM_MEASURE_COLS | number of columns
-FIELD_DELIMITER | field delimiter of the input file
-NUM_NEIGHBORS | number of nearest neighbors
-USE_DUSO_SEED | set to 'true' to use CMIC
-MAX_NUM_SUBSPACES | top subspaces used
-ALPHA | subsample's size
-NUM_SUBSAMPLING | number of subsamples
-NUM_SEEDS | number of clusters
-CANDIDATE_CUTOFF | size of beam
-MIN_PTS | used for clustering
-EPSILON | used for clustering
Default Parameter Settings CMI: Beam size M = 400 (set M = 32 for data set with less than 10 dimensions) Number of clusters Q = 10 Expected subsample size ε = 0.1 Number of subspaces = 100
Implementation and Code of CMI
We provide the executables and source code of our method in one file: cmi.jar Note that in order to access the source files, one can rename and unzip the file. For evaluation of outlier results, as in our paper, we provide an additional executable assisting in the calculation of AUC measures: run.jar (two input parameters: input file name and output file name)