SPMF documentation > Discovery of High Utility Itemsets Using a Genetic Algorithm with the HUIF-GA algorithm

This example explains how to run the HUIF-GA algorithm using the SPMF open-source data mining library.

How to run this example?

What is HUIF-GA?

HUIF-GA is an algorithm for discovering high utility itemsets (HUIs) which have utility value no less than the minimum utility threshold in a transaction database. The HUIF-GA algorithm discovers HUIs using a genetic algorithm (GA).

 What is the input?

HUIF-GA takes as input a transaction database with utility information. Let's consider the following database consisting of 7 transactions (t1,t2, ..., t7) and 5 items (1, 2, 3, 4, 5). This database is provided in the text file "contextHUIM.txt" in the package ca.pfv.spmf.tests of the SPMF distribution.

Items

Transaction utility

Item utilities for this transaction

t1

2 3 4

9

2 2 5

t2

1 2 3 4 5

18

4 2 3 5 4

t3

1 3 4

11

4 2 5

t4

3 4 5

11

2 5 4

t5

1 2 4 5

22

5 4 5 8

t6

1 2 3 4

17

3 8 1 5

t7

4 5

9

5 4

Each line of the database is:

Note that the value in the second column for each line is the sum of the values in the third column.
What are real-life examples of such a database? There are several applications in real life. One application is a customer transaction database. Imagine that each transaction represents the items purchased by a customer. The first customer named "t1" bought items 2, 3 and 4. The amount of money spent for each item is respectively 2 $, 2 $ and 5 $. The total amount of money spent in this transaction is 2 + 2 + 5 = 9 $.

What is the output?

The output of HUIF-GA is the set of high utility itemsets. An itemset X in a database D is a high-utility itemset (HUI) if and only if its utility is no less than the minimum utility threshold. For example, if we run HUIF-GA and set the minimum utility threshold to 40, we obtain 2 high utility itemsets.


itemsets

utility

{4,5}

40

{1,2,4}

41

Input file format

The input file format of high utility itemsets is defined as follows. It is a text file. Each lines represents a transaction. Each line is composed of three sections, as follows.

For example, for the previous example, the input file is defined as follows:
2 3 4:9:2 2 5
1 2 3 4 5:18:4 2 3 5 4
1 3 4:11:4 2 5
3 4 5:11:2 5 4
1 2 4 5:22:5 4 5 8
1 2 3 4:17:3 8 1 5
4 5:9:5 4

Consider the first line. It means that the transaction {2, 3, 4} has a total utility of 9 and that items 2, 3 and 4 respectively have a utility of 2, 2 and 5 in this transaction. The following lines follow the same format.

Output file format

The output file format of high utility itemsets is defined as follows. It is a text file, each following line represents a high utility itemset. On each line, the items of the itemset are first listed. Each item is represented by an integer, followed by a single space. After, all the items, the keyword " #UTILITY: " appears and is followed by the utility of the itemset. For example, we show below the output file for this example.
4 5 #UTIL: 40
1 2 4 #UTIL: 41

For example, the first line indicates that the itemset {4, 5} is a high utility itemset which has utility equals to 41. The following lines follows the same format.

Implementation details

This is the original implementation. Note that the input format is not exactly the same as described in the original article. But it is equivalent.

Where can I get more information about the HUIF-GA algorithm?

This is the reference of the article describing the HUIF-GA algorithm:

Wei Song, Chaomin Huang. Mining High Utility Itemsets Using Bio-Inspired Algorithms: A Diverse Optimal Value Framework. IEEE Access, 2018, 6(1): 19568-19582

Besides, for a general overview of high utility itemset mining, you may read this survey paper.

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