Discovery of High Utility Itemsets Using a Artificial Bee Colony Algorithm with the HUIM-ABC algorithm (SPMF documentation)

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

How to run this example?

What is HUIM-ABC?

HUIM-ABC 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 HUIM-ABC algorithm discovers HUIs using a artificial bee colony optimization algorithm (ABC). It was proposed by Wei Song et al. at PAKDD 2018.

 What is the input?

HUIM-ABC 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 HUIM-ABC 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 HUIM-ABC and set the minimum utility threshold to 40, we may obtain 2 high utility itemsets.


itemsets

utility

{4,5}

40

{1,2,4}

41

It is to be noted that the HUIM-ABC algorithm also has an optional BucketNum parameter, which should be set to 2 in this example to obtain results quickly. The BucketNum is optional and influence the search for high utility itemsets. The BucketNum parameter should be set to a small value such as 2 for dataset containing few items as in this example, and values such as 10 for datasets containing numerous items. It can have a somewhat large influence on performance and thus it can be important to set it to a proper value. The default value is 10.

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 an output file that may be obtained 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 implementaiton of HUIM-ABC. Note may not exactly the same as the input format described in the original article. But it is equivalent.

Where can I get more information about the HUIM-ABC algorithm?

This is the reference of the article describing the HUIM-ABC algorithm:

Song, W., & Huang, C. (2018) Discovering high utility itemsets based on the artificial bee colony algorithm. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 3-14). Springer, Cham.

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