Mining High-Utility Itemsets in a Transaction Database using the ULB-Miner Algorithm (SPMF documentation)
This example explains how to run the ULB-Miner algorithm using the SPMF open-source data mining library.
How to run this example?
- If you are using the graphical interface, (1) choose the "ULB-Miner" algorithm, (2) select the input file "DB_utility.txt", (3) set the output file name (e.g. "output.txt") (4) set the minimum utility to 30 and (5) click "Run algorithm".
- If you want to execute this example from the command line,
then execute this command:
java -jar spmf.jar run ULB-Miner DB_utility.txt output.txt 30 in a folder containing spmf.jar and the example input file DB_utility.txt. - If you are using the source code version of SPMF, launch the file "MainTestULB-Miner.java" in the package ca.pfv.SPMF.tests.
What is ULB-Miner?
ULB-Miner (Peng et al., 2017) is an algorithm for discovering high-utility itemsets in a transaction database containing utility information.
It is an algorithm that is an improved version of the FHM algorithm. The main improvement is that it reduces the memory usage by reusing memory using the utility-list buffer structure. This can both decrease execution time and memory usage compared to FHM. This implementation is similar to the implementation used in the paper, except that the utility-list construction procedure is the one of FHM.
What is the input?
ULB-Miner takes as input a transaction database with utility information and a minimum utility threshold min_utility (a positive integer). Let's consider the following database consisting of 5 transactions (t1,t2...t5) and 7 items (1, 2, 3, 4, 5, 6, 7). This database is provided in the text file "DB_utility.txt" in the package ca.pfv.spmf.tests of the SPMF distribution.
| Items | Transaction utility | Item utilities for this transaction | |
| t1 | 3 5 1 2 4 6 | 30 | 1 3 5 10 6 5 |
| t2 | 3 5 2 4 | 20 | 3 3 8 6 |
| t3 | 3 1 4 | 8 | 1 5 2 |
| t4 | 3 5 1 7 | 27 | 6 6 10 5 |
| t5 | 3 5 2 7 | 11 | 2 3 4 2 |
Each line of the database is:
- a set of items (the first column of the table),
- the sum of the utilities (e.g. profit) of these items in this transaction (the second column of the table),
- the utility of each item for this transaction (e.g. profit generated by this item for this transaction)(the third column of the table).
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 3, 5, 1, 2, 4 and 6. The amount of money spent for each item is respectively 1 $, 3 $, 5 $, 10 $, 6 $ and 5 $. The total amount of money spent in this transaction is 1 + 3 + 5 + 10 + 6 + 5 = 30 $.
What is the output?
The output of ULB-Miner is the set of high utility itemsets having a utility no less than a min_utility threshold (a positive integer) set by the user. To explain what is a high utility itemset, it is necessary to review some definitions. An itemset is an unordered set of distinct items. The utility of an itemset in a transaction is the sum of the utility of its items in the transaction. For example, the utility of the itemset {1 4} in transaction t1 is 5 + 6 = 11 and the utility of {1 4} in transaction t3 is 5 + 2 = 7. The utility of an itemset in a database is the sum of its utility in all transactions where it appears. For example, the utility of {1 4} in the database is the utility of {1 4} in t1 plus the utility of {1 4} in t3, for a total of 11 + 7 = 18. A high utility itemset is an itemset such that its utility is no less than min_utility For example, if we run ULB-Miner with a minimum utility of 30, we obtain 8 high-utility itemsets:
| itemsets | utility |
| {2 4} | 30 |
| {2 5} | 31 |
| {1 3 5} | 31 |
| {2 3 4} | 34 |
| {2 3 5} | 37 |
| {2 4 5} | 36 |
| {2 3 4 5} | 40 |
| {1 2 3 4 5 6} | 30 |
If the database is a transaction database from a store, we could interpret these results as all the groups of items bought together that generated a profit of 30 $ or more.
Input file format
The input file format of ULB-Miner is defined as follows. It is a text file. Each lines represents a transaction. Each line is composed of three sections, as follows.
- First, the items contained in the transaction are listed. An item is represented by a positive integer. Each item is separated from the next item by a single space. It is assumed that all items within a same transaction (line) are sorted according to a total order (e.g. ascending order) and that no item can appear twice within the same transaction.
- Second, the symbol ":" appears and is followed by the transaction utility (an integer).
- Third, the symbol ":" appears and is followed by the utility of each item in this transaction (an integer), separated by single spaces.
For example, for the previous example, the input file is defined as follows:
3 5 1 2 4 6:30:1 3 5 10 6 5
3 5 2 4:20:3 3 8 6
3 1 4:8:1 5 2
3 5 1 7:27:6 6 10 5
3 5 2 7:11:2 3 4 2
Consider the first line. It means that the transaction {3, 5, 1, 2, 4, 6} has a total utility of 30 and that items 3, 5, 1, 2, 4 and 6 respectively have a utility of 1, 3, 5, 10, 6 and 5 in this transaction. The following lines follow the same format.
Output file format
The output file format of ULB-Miner is defined as follows. It is a text file, where each 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 " #UTIL: " appears and is followed by the utility of the itemset. For example, we show below the output file for this example.
2 4 #UTIL: 30
2 5 #UTIL: 31
1 3 5 #UTIL: 31
2 3 4 #UTIL: 34
2 3 5 #UTIL: 37
2 4 5 #UTIL: 36
2 3 4 5 #UTIL: 40
1 2 3 4 5 6 #UTIL: 30
For example, the first line indicates that the itemset {2, 4} has a utility of 30. The following lines follows the same format.
Performance
High utility itemset mining is a more difficult problem than frequent itemset mining. Therefore, high-utility itemset mining algorithms are generally slower than frequent itemset mining algorithms. The ULB-Miner (2017) algorithm was proposed by improving the utility-list structure used by the FHM algorithm, and earlier algorithms such as HUI-Miner. It was shown that ULB-Miner performs well can be faster than FHM and HUI-Miner and use less memory.
Implementation details
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 ULB-Miner algorithm?
This is the reference of the article describing the ULB-Miner algorithm:
Duong, Q.H., Fournier-Viger, P., Ramampiaro, H., Norvag, K. Dam, T.-L. (2017). Effcient High Utility Itemset Mining using Buffered Utility-Lists. Applied Intelligence, Springer
Besides, for a general overview of high utility itemset mining, you may read this survey paper.