SPMF documentation > Mining the Top-K High-Utility Itemsets in a Transaction Database using the TKU Algorithm

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

How to run this example?

What is TKU?

TKU (Tseng et al., 2015) is an algorithm for discovering the top-k high-utility itemsets in a transaction database containing utility information.

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?

TKU takes as input a transaction database with utility information and a parameter k (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:

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 TKU is the top-k high utility itemsets, that is the k itemsets that have the highest utility in the transaction database taken as input. To explain what is a top-k 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. The top-k high utility itemsets is the set of the k itemsets that have the highest utility. It is to be noted that in some cases, it is possible that the algorithm returns more than k itemsets if several itemsets have the same utility. For example, if we run TKU with a k = 8, we obtain the following top-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 k groups of items bought together that generated the highest profit.

Input file format

The input file format of TKU 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 TKU is defined as follows. It is a text file, where each line represents a top-k 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.

4 2 #UTIL: 30
2 5 #UTIL: 31
4 5 3 2 #UTIL: 40
4 5 2 #UTIL: 36
2 5 3 #UTIL: 37
4 2 3 #UTIL: 34
1 5 3 #UTIL: 31
6 5 4 3 2 1 #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

Top-k high utility itemset mining is a more difficult problem than high-utility itemset mining. Therefore, top-k high-utility itemset mining algorithms are generally slower than high utility itemset mining algorithms. Besides, TKU is the first top-k high utility itemset mining algorithm. It is not the fastest algoritm. Besides, this implementation is the original implementation but it is not implemented in a very efficient way as it perform many disk accesses to store intermediate results to disk rather than keeping them into memory (which would be more efficient). The code of TKU was obtained from the UP-Miner project under the GPL license, and was modified to be integrated into SPMF.

Implementation details

The version offered in SPMF is the original implementation of TKU, adapted to be integrated into SPMF.

Note that the input format is not exactly the same as described in the article. But it is equivalent.

Where can I get more information about the TKU algorithm?

The most recent paper describing the TKU algorithm is:

Tseng, V., Wu, C., Fournier-Viger, P., Yu, P. S. (2015). Efficient Algorithms for Mining Top-K High Utility Itemsets. IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(1): 54-67.

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

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