Mining High-Utility Itemsets in a Transaction Database using the UPGrowth / UPGrowth+ Algorithms (SPMF documentation)
This example explains how to run the UPGrowth / UPGrowth+ algorithms using the SPMF open-source data mining library.
How to run this example?
- If you are using the graphical interface, (1) choose the "UPGrowth" or "UPGrowth+" 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 UPGrowth 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 "MainTestUPGrowth.java" in the package ca.pfv.SPMF.tests.
What is UPGrowth?
UP-Growth (Tseng et al., KDD 2010) is an algorithm for discovering high-utility itemsets in a transaction database containing utility information. UP-Growth+ (Tseng et al., KDD 2012) is an improved version.
Those two algorithms are important algorithms because they introduce some interesting ideas. However, recently some more efficient algorithms have been proposed such as FHM (2014) and HUI-Miner (2012). These latter algorithms were shown to be more than 100 times faster than UP-Growth+ in some cases, and are also offered in SPMF.
What is the input?
UP-Growth 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 UP-Growth 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 UP-Growth with a minimum utility of 30, we obtain 8 high-utility itemsets:
| itemsets | utility | support |
| {2 4} | 30 | 40 % (2 transactions) |
| {2 5} | 31 | 60 % (3 transactions) |
| {1 3 5} | 31 | 40 % (2 transactions) |
| {2 3 4} | 34 | 40 % (2 transactions) |
| {2 3 5} | 37 | 60 % (3 transactions) |
| {2 4 5} | 36 | 40 % (2 transactions) |
| {2 3 4 5} | 40 | 40 % (2 transactions) |
| {1 2 3 4 5 6} | 30 | 20 % (1 transactions) |
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 UP-Growth 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 UP-Growth 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 UP-Growth (2010) algorithm was the fastest algorithm for high-utility itemset mining in 2010. However, more efficient algorithm have been proposed. The HUI-Miner (2012) was shown to be up to 100 times faster than UPGrowth, and more recently the FHM algorithm (2014) was shown to be up to six times faster than HUI-Miner. More recently, the EFIM algorithm (2015) was proposed and was shown to outperform UPGrowth+ and other recent algorithms such as FHM (2014), HUI-Miner (2012), HUP-Miner (2014). All these algorithms are offered in SPMF (see "performance" page of this website).
Implementation details
The version implemented here contains all the optimizations described in the paper proposing UP-Growth (strategies DGU, DGN, DLU and DLN). 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 UP-Growth algorithm?
This is the reference of the article describing the UP-Growth algorithm:
V S. Tseng, C.-W. Wu, B.-E. Shie, P. S. Yu: UP-Growth: an efficient algorithm for high utility itemset mining. KDD 2010: 253-262
V. S. Tseng, B.-E. Shie, C.-W. Wu, and P. S. Yu. Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Transactions on Knowledge and Data Engineering, 2012, doi: 10.1109/TKDE.2012.59.
Besides, for a general overview of high utility itemset mining, you may read this survey paper.