Efficient mining of association rules using closed itemset lattices

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

doi:10.1016/s0306-4379(99)00003-4

Cited by 617 publications

(407 citation statements)

References 3 publications

Supporting

Mentioning

398

Contrasting

Unclassified

Order By: Relevance

“…The first set of algorithms that were explicitly designed to compute frequent closed itemsets were Close [39], Apriori-Close [37] and A-Close [38]. Inspired by Apriori [2], all these algorithms traverse the database in a level-wise approach.…”

Section: Algorithms For Computing Frequent Closed / Key Itemsetsmentioning

confidence: 99%

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

Section: Algorithms For Computing Frequent Closed / Key Itemsetsmentioning

confidence: 99%

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 concept of closed set is classical within lattice theory and has been studied for association rule mining since the definition of the Close algorithm in [17]. The collection of (frequent) closed itemsets is a useful condensed representation of the (frequent) itemsets in the case of highly correlated data [4].…”

Section: Closed and Free Itemsetsmentioning

confidence: 99%

“…This algorithm is an extension of the Close algorithm described in [17] in which only the frequency constraint is considered.…”

Section: Mining Constrained Closed Itemsetsmentioning

confidence: 99%

“…Details about Step 1 can be found in previous works on closed itemsets (see, e.g., [17]) and are not provided here.…”

Section: := {(S Freq(s)) ∃C ∈ O St S ⊆ C} 2 Output({(s Freq(s)mentioning

confidence: 99%

“…frequency queries [11] is useful. Several researchers have studied the efficient computation of condensed representations of (frequent) itemsets like the closed sets [17,4,20], the free sets [5] or the disjunct free sets [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Condensed Representations for Interactive Association Rule Mining

Jeudy

Boulicaut

2002

Principles of Data Mining and Knowledge Discovery

View full text Add to dashboard Cite

Abstract. Association rule mining is a popular data mining task. It has an interactive and iterative nature, i.e., the user has to refine his mining queries until he is satisfied with the discovered patterns. To support such an interactive process, we propose to optimize sequences of queries by means of a cache that stores information from previous queries. Unlike related works, we use condensed representations like free and closed itemsets for both data mining and caching. This results in a much more efficient mining technique in highly correlated data and a much smaller cache than in previous approaches.

show abstract