SPMF documentation > Mining High-Utility Itemsets in a Transaction Database using the HUP-Miner Algorithm

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

How to run this example?

What is HUP-Miner?

HUP-Miner (Krishnamoorthy, 2014) is an extension of the HUI-Miner algorithm (Liu & Qu, CIKM 2012) for discovering high-utility itemsets in a transaction database containing utility information. It introduces the idea of partitioning the database and another pruning strategy named LA-prune. A drawback of HUP-Miner is that the user needs to set an additional parameter, which is the number of partitions. Moreover, according to our experiments, HUP-Miner is faster than HUI-Miner but slower than FHM.

High utility itemset mining has several applications such as discovering groups of items in transactions of a store that generate the most profit. A database containing utility information is a database where items can have quantities and a unit price. Although these algorithms are often presented in the context of market basket analysis, there exist other applications.

What is the input?

HUP-Miner takes as input a transaction database with utility information, a minimum utility threshold min_utility (a positive integer) and a number of partitions k.

Note that the parameter k determines how much partitions HUP-Miner uses internally, which influence the performance of HUP-Miner but has no effect on the output of the algorithm. A typical value for k could be 10. However, the optimal value for k may be found empirically for each dataset.

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:

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 HUP-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 HUP-Miner 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 HUP-Miner 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:

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 HUP-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 HUI-Miner algorithm was reported as one of the most efficient algorithm for high utility itemset mining. HUP-Miner is an extension of HUI-Miner, just like FHM. These two latter are faster than HUI-Miner. However, HUP-Miner introduce a new parameter which is the number of partitions. In our experiment, FHM is faster than HUP-Miner. More recently, the EFIM algorithm (2015) was proposed and was shown to outperform HUP-Miner, 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 HUP-Miner. 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 HUP-Miner algorithm?

This is the reference of the article describing the HUP-Miner algorithm:

Krishnamoorthy, S. (2014). Pruning Strategies for Mining High-Utility Itemsets. Expert Systems with Applications.

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

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