Itemset mining: A constraint programming perspective

The field of data mining has become accustomed to specifying constraints on patterns of interest. A large number of systems and techniques has been developed for solving such constraint-based mining problems, especially for mining itemsets. The approach taken in the field of data mining contrasts wi...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Artificial intelligence Ročník 175; číslo 12; s. 1951 - 1983
Hlavní autoři: Guns, Tias, Nijssen, Siegfried, De Raedt, Luc
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier B.V 01.08.2011
Elsevier
Témata:
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Constraint programming
Data mining
Data processing. List processing. Character string processing
Exact sciences and technology
Expert systems
Itemset mining
Mathematical models
Memory organisation. Data processing
Mining
Programming
Software
State of the art
Tasks
Data mining
Itemset mining
Constraint programming
Data analysis
Declarative language
Algorithmics
Constraint satisfaction
Modeling
Search algorithm
Constrained optimization
High level language
Data field
Electronic trade
Artificial intelligence
ISSN:0004-3702, 1872-7921
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The field of data mining has become accustomed to specifying constraints on patterns of interest. A large number of systems and techniques has been developed for solving such constraint-based mining problems, especially for mining itemsets. The approach taken in the field of data mining contrasts with the constraint programming principles developed within the artificial intelligence community. While most data mining research focuses on algorithmic issues and aims at developing highly optimized and scalable implementations that are tailored towards specific tasks, constraint programming employs a more declarative approach. The emphasis lies on developing high-level modeling languages and general solvers that specify what the problem is, rather than outlining how a solution should be computed, yet are powerful enough to be used across a wide variety of applications and application domains. This paper contributes a declarative constraint programming approach to data mining. More specifically, we show that it is possible to employ off-the-shelf constraint programming techniques for modeling and solving a wide variety of constraint-based itemset mining tasks, such as frequent, closed, discriminative, and cost-based itemset mining. In particular, we develop a basic constraint programming model for specifying frequent itemsets and show that this model can easily be extended to realize the other settings. This contrasts with typical procedural data mining systems where the underlying procedures need to be modified in order to accommodate new types of constraint, or novel combinations thereof. Even though the performance of state-of-the-art data mining systems outperforms that of the constraint programming approach on some standard tasks, we also show that there exist problems where the constraint programming approach leads to significant performance improvements over state-of-the-art methods in data mining and as well as to new insights into the underlying data mining problems. Many such insights can be obtained by relating the underlying search algorithms of data mining and constraint programming systems to one another. We discuss a number of interesting new research questions and challenges raised by the declarative constraint programming approach to data mining.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2011.05.002