Probabilistic Itemset Mining
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README.md

IIM: Interesting Itemset Miner Build Status

IIM is a novel algorithm that mines the itemsets that are most interesting under a probablistic model of transactions. Our model is able to efficiently infer interesting itemsets directly from the transaction database.

This is an implementation of the itemset miner from our paper:
A Bayesian Network Model for Interesting Itemsets
J. Fowkes and C. Sutton. PKDD 2016.

Installation

Installing in Eclipse

Simply import as a maven project into Eclipse using the File -> Import... menu option (note that this requires m2eclipse).

It's also possible to export a runnable jar from Eclipse using the File -> Export... menu option.

Compiling a Runnable Jar

To compile a standalone runnable jar, simply run

mvn package

in the top-level directory (note that this requires maven). This will create the standalone runnable jar itemset-mining-1.0.jar in the itemset-mining/target subdirectory. The main class is itemsetmining.main.ItemsetMining (see below).

Running IIM

IIM uses a Bayesian Network Model to determine which itemsets are the most interesting in a given dataset.

Mining Interesting Itemsets

Main class itemsetmining.main.ItemsetMining mines itemsets from a specified transaction database file. It has the following command line options:

  • -f   database file to mine (in FIMI format)
  • -i   max. no. iterations
  • -s   max. no. structure steps
  • -r   max. runtime (min)
  • -l   log level (INFO/FINE/FINER/FINEST)
  • -v   print to console instead of log file

See the individual file javadocs in itemsetmining.main.ItemsetMining for information on the Java interface. In Eclipse you can set command line arguments for the IIM interface using the Run Configurations... menu option.

Example Usage

A complete example using the command line interface on a runnable jar. We can mine the provided example dataset example.dat as follows:

$ java -cp itemset-mining/target/itemset-mining-1.0.jar itemsetmining.main.ItemsetMining     
 -i 100
 -f example.dat 
 -v 

which will output to the console. Omitting the -v flag will redirect output to a log-file in /tmp/.

Input/Output Formats

Input Format

IIM takes as input a transaction database file in FIMI format. The FIMI format is very simple: each line of the input file represents a transaction and each transaction is a space-seperated list of items, represented by positive integers. The FIMI format requires the transaction items to be listed in increasing order and does not allow duplicate items (however IIM is not sensitive to item order and ignores item duplicates). For example, a few lines (transactions) from example.dat are:

6 10 22 31 32 41 52 
2 12 14 26 50 
3 18 25 31 34 38 63 
17 28 30 37 
16 19 45 46 49 51 52 54 56 65 

Note that any other item formats (e.g. words for text corpora) need to be manually mapped to (and from) positive integers by means of a dictionary.

Output Format

IIM outputs a list of interesting itemsets, one itemset per line, ordered first by their interestingness (given in the 'int' column) followed by their probability (given in the 'prob' column). For example, the first few lines of output for the usage example above are:

============= INTERESTING ITEMSETS =============
{18}    prob: 0.34830   int: 1.00000 
{14}    prob: 0.13740   int: 1.00000 
{5}     prob: 0.11740   int: 1.00000 
{16}    prob: 0.09110   int: 1.00000 
{6, 7, 22, 36, 65, 67}  prob: 0.08440   int: 1.00000 
{17, 28, 30, 37}        prob: 0.07830   int: 1.00000 
{1, 2, 8, 11, 12, 13, 20, 63, 64}       prob: 0.07670   int: 1.00000 
{59, 60, 62}    prob: 0.06980   int: 1.00000 
{43, 46, 55}    prob: 0.06890   int: 1.00000 
{53}    prob: 0.06870   int: 1.00000 

See the accompanying paper for details of how to interpret 'interestingness' and 'probability' under IIM's probabilistic model.

Spark Implementation

IIM also has a (beta) parallel implemetation using Spark in Standalone Mode with an HDFS filesystem (see e.g. relevant parts of this tutorial).

Configuring Spark Options

Basic IIM configuration for Spark and HDFS must be set in itemset-miner/src/main/resources/spark.properties (see the example config provided):

  • SparkHome   Spark home directory
  • SparkMaster   URL of spark master server
  • MachinesInCluster   No. machines in the cluster
  • HDFSMaster   URL of HDFS master server
  • HDFSConfFile   Location of Hadoop core-site.xml

Mining Itemsets using Spark

Main class itemsetmining.main.SparkItemsetMining mines itemsets using a Standalone Spark Sever. It has the following additional command line options:

  • -c   no. Spark cores to use
  • -j   location of IIM standalone jar (default is itemset-mining/target/itemset-mining-1.0.jar)

See the individual file javadocs in itemsetmining.main.SparkItemsetMining for information on the Java interface.

Example Usage

A complete Spark example using the command line interface is as follows:

$ java -cp itemset-mining/target/itemset-mining-1.0.jar itemsetmining.main.SparkItemsetMining
 -c 16  
 -i 100
 -f example.dat 
 -v 

which will output to the console. Omitting the -v flag will redirect output to a log-file in /tmp/.

Bugs

Please report any bugs using GitHub's issue tracker.

License

This algorithm is released under the GNU GPLv3 license.