
A onephase treebased algorithm for mining highutility itemsets from a transaction database
Highutility itemset mining finds itemsets from a transaction database w...
read it

TOPIC: Topk HighUtility Itemset Discovering
Utilitydriven itemset mining is widely applied in many realworld scena...
read it

Onshelf Utility Mining of Sequence Data
Utility mining has emerged as an important and interesting topic owing t...
read it

Discovering High Utility Episodes in Sequences
Sequence data, e.g., complex event sequence, is more commonly seen than ...
read it

TargetUM: Targeted HighUtility Itemset Querying
Traditional highutility itemset mining (HUIM) aims to determine all hig...
read it

Competitive Information Disclosure with Multiple Receivers
This paper analyzes a model of competition in Bayesian persuasion in whi...
read it

Utility Elicitation as a Classification Problem
We investigate the application of classification techniques to utility e...
read it
Highutility itemset mining for subadditive monotone utility functions
Highutility Itemset Mining (HUIM) finds itemsets from a transaction database with utility no less than a userdefined threshold where the utility of an itemset is defined as the sum of the utilities of its items. In this paper, we introduce the notion of generalized utility functions that need not be the sum of individual utilities. In particular, we study subadditive monotone (SM) utility functions and prove that it generalizes the HUIM problem mentioned above. Moving on to HUIM algorithms, the existing algorithms use upperbounds like `Transaction Weighted Utility' and `ExactUtility, Remaining Utility' for efficient searchspace exploration. We derive analogous and tighter upperbounds for SM utility functions and explain how existing HUIM algorithms of different classes can be adapted using our upper bound. We experimentally compared adaptations of some of the latest algorithms and point out some caveats that should be kept in mind while handling general utility functions.
READ FULL TEXT
Comments
There are no comments yet.