Mining Frequent Patterns in Uncertain Transactional Databases

What is frequent pattern mining?

Frequent pattern mining aims to discover all interesting patterns in a transactional database that have support no less than the user-specified minimum support (minSup) constraint. The minSup controls the minimum number of transactions that a pattern must appear in a database.

What is a uncertain transactional database?

A transactional database is a collection of transactions, where each transaction contains a transaction-identifier and a set of items with their respective uncertain value.
A hypothetical transactional database containing the items a, b, c, d, e, f, and g as shown below.

tid	Transactions
1	a(0.4) b(0.5) c(0.2) g(0.1)
2	b(0.2) c(0.3) d(0.4) e(0.2)
3	a(0.3) b(0.1) c(0.3) d(0.4)
4	a(0.2) c(0.6) d(0.2) f(0.1)
5	a(0.3) b(0.2) c(0.4) d(0.5) g(0.3)
6	c(0.2) d(0.7) e(0.34) f(0.2)
7	a(0.6) b(0.4) c(0.3) d(0.2)
8	a(0.2) e(0.2) f(0.2)
9	a(0.1) b(0.3) c(0.2) d(0.4)
10	b(0.3) c(0.2) d(0.1) e(0.6)

Note: Duplicate items must not exist in a transaction.

What is the acceptable format of a uncertain transactional databases in PAMI?

Each row in a transactional database must contain only items with their respective uncertain values. A sample transactional database, say sampleInputFile.txt, is provided below.

a(0.4) b(0.5) c(0.2) g(0.1)
b(0.2) c(0.3) d(0.4) e(0.2)
a(0.3) b(0.1) c(0.3) d(0.4)
a(0.2) c(0.6) d(0.2) f(0.1)
a(0.3) b(0.2) c(0.4) d(0.5) g(0.3)
c(0.2) d(0.7) e(0.34) f(0.2)
a(0.6) b(0.4) c(0.3) d(0.2)
a(0.2) e(0.2) f(0.2)
a(0.1) b(0.3) c(0.2) d(0.4)
b(0.3) c(0.2) d(0.1) e(0.6)

What are the input parameters?

The input parameters to a frequent pattern mining algorithm are:

Transactional database
Acceptable formats:
- String : E.g., ‘uncertainTransactionalDatabase.txt’
- URL : E.g., https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/transactional_T10I4D100K.csv
- DataFrame with the header titled ‘Transactions’
minSup
specified in
- count (beween 0 to length of a database) or
- [0, 1]
seperator
default seperator is ‘\t’ (tab space)

How to store the output of a frequent pattern mining algorithm?

The patterns discovered by a frequent pattern mining algorithm can be saved into a file or a data frame.

How to run the frequent pattern mining algorithms in a terminal?

Download the PAMI source code from github.
Unzip the PAMI source code folder and enter into uncertain frequent pattern folder.
Enter into uncertainFrequentPattern folder
You will find folder like basic
Enter into a specific folder and execute the following command on terminal.

syntax: python3 algorithmName.py <path to the input file> <path to the output file> <minSup> <seperator>

Example: python3 PUFGrowth.py inputFile.txt outputFile.txt 0.05 ' '

How to execute a frequent pattern mining algorithm in a Jupyter Notebook?

Import the PAMI package executing: pip3 install PAMI

Install the PAMI package from the PYPI repository by executing the following command: pip3 install PAMI
Run the below sample code by making necessary changes

import PAMI.uncertainFrequentPattern.basic.PUFGrowth as alg 

iFile = 'sampleInputFile.txt'  #specify the input transactional database <br>
minSup = 0.5                  #specify the minSup value <br>
seperator = ' '                #specify the seperator. Default seperator is tab space. <br>
oFile = 'frequentPatterns.txt'   #specify the output file name<br>

obj = alg.PUFGrowth(iFile, minSup, seperator) #initialize the algorithm <br>
obj.startMine()                       #start the mining process <br>
obj.savePatterns(oFile)               #store the patterns in file <br>
df = obj.getPatternsAsDataFrame()     #Get the patterns discovered into a dataframe <br>
obj.printStats()                      #Print the stats of mining process

The frequentPatterns.txt file contains the following patterns (format: pattern:support):!cat frequentPatterns.txt

!cat frequentPatterns.txt

f 0.5
e 1.3399999999999999
b 2.0
b a 0.56
b c 0.51
a 2.099999999999996
a c 0.6100000000000001
c 2.7
d 2.9000000000000004
c d 0.8600000000000001

The dataframe containing the patterns is shown below:

df

	Patterns	Support
0	f	0.50
1	e	1.34
2	b	2.00
3	b a	0.56
4	b c	0.51
5	a	2.09
6	a c	0.61
7	c	2.70
8	d	2.90
9	c d	0.86