Mining Skyline Frequent-Utility Patterns using the SFUPMinerUemax algorithm (SPMF documentation)

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

How to run this example?

What is SFUPMinerUemax?

SFUPMinerUemax is an algorithm for discovering skyline frequent-utility patterns (SFUPs) in a transaction database containing utility information. The SFUPMinerUemax algorithm discovers SFUPs by exploring a utility-list structure using a depth-first search. An efficient pruning strategy is also developed to prune unpromising candidates early and thus reduce the search space

 What is the input?

SFUPMinerUemax 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.


Transaction utility

Item utilities for this transaction


2 3 4


2 2 5


1 2 3 4 5


4 2 3 5 4


1 3 4


4 2 5


3 4 5


2 5 4


1 2 4 5


5 4 5 8


1 2 3 4


3 8 1 5


4 5


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 SFUPMinerUemax is the set of skyline frequent-utility patterns. An itemset X in a database D is a skyline frequent-utility patterns (SFUP) iff it is not dominated by any other itemset in the database by considering both the frequent and utility factors. An itemset X dominates another itemset Y in D, iff f(X) >= f(Y) and u(X) >= u(Y). For example, if we run SFUPMinerUemax, we obtain 3 skyline frequent-utility patterns.













Input file format

The input file format of SFUPMinerUemax 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 the algorithm is defined as follows. It is a text file, the first line record the count of skyline frequent-utility patterns. And each following line represents a skyline frequent-utility pattern. 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 "#SUP:" appears, which is followed by a integer value indicating the support of that itemset and the keyword " #UTILITY: " appears and is followed by the utility of the itemset. For example, we show below the output file for this example.
Total skyline frequent-utility itemset: 3
2 1 4 #SUP:3 #UTILITY:41
5 4 #SUP:4 #UTILITY:40
4 #SUP:7 #UTILITY:35

For example, the first line indicates that there are 3 skyline frequent-utility patterns in the example. The second line indicates that the itemset {2, 1, 4} is a skyline frequent-utility itemset which has support equals to 3 and utility equals to 41. The following lines follows the same format.

Implementation details

The version implemented here contains all the optimizations described in the paper proposing SFUPMinerUemax. 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 SFUPMinerUemax algorithm?

This is the reference of the article describing the SFUPMinerUemax algorithm:

Jerry Chun-Wei Lin, Lu Yang, Philippe Fournier-Viger, Siddharth Dawar, Vikram Goyal, Ashish Sureka, and Bay Vo, “A More Efficient Algorithm to Mine Skyline Frequent-Utility Patterns,” International Conference on Genetic and Evolutionary Computing, 2016. (ICGEC 2016)

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