Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis

Stumme, Gerd; Taouil, Rafik; Bastide, Yves; Pasquier, Nicolas; Lakhal, Lotfi

doi:10.1007/3-540-45422-5_24

Cited by 67 publications

(46 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Here we will show by an example (taken from [46]), how these bases look like. A general overview is given in the next section.…”

Section: Bases Of Association Rulesmentioning

confidence: 99%

“…Edges without label indicate that the confidence of the rule is below the minimum confidence threshold. The visualization technique is described in more detail in [46]. In comparison with other visualization techniques for association rules (as for instance implemented in the IBM Intelligent Miner), the visualization of the Luxenburger basis within the iceberg concept lattice benefits of the smaller number of rules to be represented (without loss of information!…”

Section: Bases Of Association Rulesmentioning

confidence: 99%

“…Their results were rediscovered from the data mining community [36,53] and adapted to the frequency constraint [37,44,47,55]. This approach is described in detail in [46], and sketched above in Section 5. Other approaches comprise the following:…”

Section: Bases Of Association Rulesmentioning

confidence: 99%

“…5 can be deduced from the rest. Since the deduction rules for this computation turn out to be rather complex for a knowledge discovery application, we decided in [46] to work with a simpler calculus.…”

Section: Bases Of Association Rulesmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Mining of Association Rules Based on Formal Concept Analysis

Lakhal

Stumme

2005

Formal Concept Analysis

Self Cite

View full text Add to dashboard Cite

Abstract. Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, Titanic, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.

show abstract

“…Here we will show by an example (taken from [46]), how these bases look like. A general overview is given in the next section.…”

Section: Bases Of Association Rulesmentioning

confidence: 99%

Section: Bases Of Association Rulesmentioning

confidence: 99%

Section: Bases Of Association Rulesmentioning

confidence: 99%

Section: Bases Of Association Rulesmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Mining of Association Rules Based on Formal Concept Analysis

Lakhal

Stumme

2005

Formal Concept Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…The resulting set produces the so called minmax basis of association rules. In order to compute the min-max basis of Alpha association rules, starting from the Iceberg Alpha lattice (or equivalently from the set of Alpha closed terms), we have straightforwardly adapted the method proposed in [10]. All these features are implemented in the software AlphabetaGalicia 5 …”

Section: Class Incrementality Of Alpha Latticesmentioning

confidence: 99%

Incremental Construction of Alpha Lattices and Association Rules

Soldano

Ventos

Champesme

et al. 2010

Knowledge-Based and Intelligent Information and Engineering Systems

View full text Add to dashboard Cite

Abstract. In this paper we discuss Alpha Galois lattices (Alpha lattices for short) and the corresponding association rules. An alpha lattice is coarser than the related concept lattice and so contains fewer nodes, so fewer closed patterns, and a smaller basis of association rules. Coarseness depends on a a priori classification, i.e. a cover C of the powerset of the instance set I, and on a granularity parameter α. In this paper, we define and experiment a Merge operator that when applied to two Alpha lattices G(C1, α) and G(C2, α) generates the Alpha lattice G(C1∪C2, α), so leading to a class-incremental construction of Alpha lattices. We then briefly discuss the implementation of the incremental process and describe the min-max bases of association rules extracted from Alpha lattices.

show abstract

Formal Concept Analysis on Its Way from Mathematics to Computer Science

Stumme

2002

Conceptual Structures: Integration and Interfaces

View full text Add to dashboard Cite

Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis

Cited by 67 publications

References 21 publications

Efficient Mining of Association Rules Based on Formal Concept Analysis

Efficient Mining of Association Rules Based on Formal Concept Analysis

Incremental Construction of Alpha Lattices and Association Rules

Formal Concept Analysis on Its Way from Mathematics to Computer Science

Contact Info

Product

Resources

About