Paper Info
Title
Discovering High-Utility Itemsets at Multiple Abstraction Levels.
Abstract
High-Utility Itemset Mining (HUIM) is a relevant data mining task. The goal is to discover recurrent combinations of items characterized by high profit from transactional datasets. HUIM has a wide range of applications among which market basket analysis and service profiling. Based on the observation that items can be clustered into domain-specific categories, a parallel research issue is generalized itemset mining. It entails generating correlations among data items at multiple abstraction levels. The extraction of multiple-level patterns affords new insights into the analyzed data from different viewpoints. This paper aims at discovering a novel pattern that combines the expressiveness of generalized and High-Utility itemsets. According to a user-defined taxonomy items are first aggregated into semantically related categories. Then, a new type of pattern, namely the Generalized High-utility Itemset (GHUI), is extracted. It represents a combinations of items at different granularity levels characterized by high profit (utility). While profitable combinations of item categories provide interesting high-level information, GHUIs at lower abstraction levels represent more specific correlations among profitable items. A single-phase algorithm is proposed to efficiently discover utility itemsets at multiple abstraction levels. The experiments, which were performed on both real and synthetic data, demonstrate the effectiveness and usefulness of the proposed approach.
Year
Venue
Field
2017
ADBIS (Short Papers and Workshops)
Data mining,Abstraction,Viewpoints,Profiling (computer programming),Computer science,Synthetic data,Knowledge extraction,Granularity,Affinity analysis,Database,Expressivity
DocType
Citations 
PageRank 
Conference
2
0.40
References 
Authors
8
4
Name
Order
Citations
PageRank
Luca Cagliero128531.63
Silvia Chiusano234742.57
Paolo Garza342639.13
Giuseppe Ricupero420.40