Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 1.939

A survey of incremental high‐utility itemset mining

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items. However, most HUIM algorithms are designed to be applied to static databases. But in real‐world applications such as market basket analysis and business decision‐making, databases are often dynamically updated by inserting new data such as customer transactions. Several researchers have proposed algorithms to discover high‐utility itemsets (HUIs) in dynamically updated databases. Unlike batch algorithms, which always process a database from scratch, incremental high‐utility itemset mining (iHUIM) algorithms incrementally update and output HUIs, thus reducing the cost of discovering HUIs. This paper provides an up‐to‐date survey of the state‐of‐the‐art iHUIM algorithms, including Apriori‐based, tree‐based, and utility‐list‐based approaches. To the best of our knowledge, this is the first survey on the mining task of incremental high‐utility itemset mining. The paper also identifies several important issues and research challenges for iHUIM. WIREs Data Mining Knowl Discov 2018, 8:e1242. doi: 10.1002/widm.1242

This article is categorized under:

  • Algorithmic Development > Association Rules
  • Application Areas > Data Mining Software Tools
  • Fundamental Concepts of Data and Knowledge > Knowledge Representation
Taxonomy of high‐utility itemset mining (HUIM) algorithm
[ Normal View | Magnified View ]
The construction and updating process of the HUI‐trie in the incremental updated database. (a) HUI‐trie up to T 6 (b) HUI‐trie up to T 8, and (c) HUI‐trie up to T 10
[ Normal View | Magnified View ]
Restructuring and updating the high‐utility patterns in incremental databases tree (HUPID‐tree) using the tail‐node information list (TIList)
[ Normal View | Magnified View ]
Construction of the initial high‐utility patterns in incremental databases tree (HUPID‐tree) for the original database D
[ Normal View | Magnified View ]
Construction and updating operations of the incremental compressed high‐utility mining (iCHUM)‐tree (a) after inserting T 6, (b) after inserting T 8, and (c) after inserting T 10
[ Normal View | Magnified View ]
The construction of utility‐lists of k‐itemsets
[ Normal View | Magnified View ]
The constructed utility‐lists of one itemsets for the updated database U = {Dd 1 + d 2 + }
[ Normal View | Magnified View ]
Flowchart of the HUI‐list‐INS algorithm
[ Normal View | Magnified View ]
The constructed utility‐lists of one itemsets for the original database D
[ Normal View | Magnified View ]
Flowchart of the PRE‐HUI‐INS algorithm (Lin et al., )
[ Normal View | Magnified View ]
Updated nine cases for incremental high‐utility itemset mining (iHUIM) with the utility‐based pre‐large concept (Lin et al., )
[ Normal View | Magnified View ]
Updated four cases for incremental high‐utility itemset mining (iHUIM) with the utility‐based Fast‐UPdated (FUP) concept (Lin et al., )
[ Normal View | Magnified View ]
Construction and restructuring operations of the IHUPTWU‐tree. (a) IHUPTWU‐tree up to T 6 (the IHUPTWU‐tree for the original database D), (b) IHUPTWU‐tree up to T 8 (the updated IHUPTWU‐tree for the updated database {Dd 1 + }), and (c) IHUPTWU‐tree up to T 10 (the updated IHUPTWU‐tree for the updated database U = {Dd 1 + d 2 + })
[ Normal View | Magnified View ]
Taxonomy of incremental high‐utility itemset mining (iHUIM) algorithm
[ Normal View | Magnified View ]

Browse by Topic

Fundamental Concepts of Data and Knowledge > Knowledge Representation
Algorithmic Development > Association Rules
Application Areas > Data Mining Software Tools

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts