Using Condensed Representations for Interactive Association Rule Mining

Jeudy, Baptiste; Boulicaut, Jean-François

doi:10.1007/3-540-45681-3_19

Cited by 21 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For future work, we seek to optimize the performance of AR querying by considering a caching system (Jeudy and Boulicaut, 2002), as well as to further reduce the number of rules by pruning the non-actionable ARs (Liu et al, 2001). …”

Section: Discussionmentioning

confidence: 99%

“…Using concise representations for interactive AR mining is also proposed in (Jeudy and Boulicaut, 2002), which applies free-sets (Boulicaut et al, 2003) to achieve a much smaller cache for optimizing AR queries in an interactive process. While caching can also be applied using δ-TCFIs, this paper proposes the δ-TAR tree to support efficient interactive AR querying.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Effective elimination of redundant association rules

Cheng

2007

Data Min Knowl Disc

View full text Add to dashboard Cite

Abstract. It is well-recognized that the main factor that hinders the applications of Association Rules (ARs) is the huge number of ARs returned by the mining process. In this paper, we propose an effective solution that presents concise mining results by eliminating the redundancy in the set of ARs. We adopt the concept of δ-Tolerance to define the set of δ-Tolerance ARs (δ-TARs), which is a concise representation for the set of ARs. δ-Tolerance is a relaxation on the closure defined on the support of frequent itemsets, thus allowing us to effectively prune the redundant ARs. We then devise a set of inference rules, with which we prove that the set of δ-TARs is a non-redundant representation of ARs. In addition, we prove that the set of ARs that is derived from the δ-TARs by the inference rules is sound and complete. We also develop a compact tree structure called the δ-TAR tree, which facilitates the efficient generation of the δ-TARs and derivation of other ARs. Experimental results verify the efficiency of using the δ-TAR tree to generate the δ-TARs and to query the ARs. The set of δ-TARs is also shown to be drastically smaller than the state-of-the-art concise representations of ARs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Effective elimination of redundant association rules

Cheng

2007

Data Min Knowl Disc

View full text Add to dashboard Cite

show abstract

“…Most of these works require that some strong relation holds between the queries like inclusion or equivalence. Caching condensed representations seems quite natural and we began to study the use of free itemsets for that purpose [18]. In [18], we assume the user defines constraints on closed sets and can refine them in a sequence of queries.…”

Section: Uses Of Condensed Representationsmentioning

confidence: 99%

“…Caching condensed representations seems quite natural and we began to study the use of free itemsets for that purpose [18]. In [18], we assume the user defines constraints on closed sets and can refine them in a sequence of queries. Free sets from previous queries are put in a cache.…”

Section: Uses Of Condensed Representationsmentioning

confidence: 99%

Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining

Jeudy

Boulicaut

2002

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Recently inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. Querying these databases needs for primitives to: (1) select, manipulate and query data, (2) select, manipulate and query "interesting" patterns (i.e., those patterns that satisfy certain constraints), and (3) cross over patterns and data (e.g., selecting the data in which some patterns hold). Designing such query languages is a long-term goal and only preliminary approaches have been studied, mainly for the association rule mining task. Starting from a discussion on the MINE RULE operator, we identify several open issues for the design of inductive databases dedicated to these descriptive rules. These issues concern not only the offered primitives but also the availability of efficient evaluation schemes. We emphasize the need for primitives that work on more or less condensed representations for the frequent itemsets, e.g., the (frequent) δ-free and closed itemsets. It is useful not only for optimizing single association rule mining queries but also for sophisticated post-processing and interactive rule mining.

show abstract

“…Methods falling into that category that have been studied in the context of frequent itemset discovery are: incremental mining [7], caching intermediate query results [17], and reusing materialized complete [5][15] [16] or condensed [13] results of previous queries provided that syntactic differences between the queries satisfy certain conditions.…”

Section: Related Workmentioning

confidence: 99%

Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth

Wojciechowski

Galecki

Gawronek

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and tested on the Apriori algorithm. In this paper we discuss and experimentally evaluate three strategies for concurrent processing of frequent itemset queries using FP-growth as a basic frequent itemset mining algorithm. The first strategy is Mine Merge, which does not depend on a particular mining algorithm and can be applied to FP-growth without modifications. The second is an implementation of the general idea of Common Counting for FP-growth. The last is a completely new strategy, motivated by identified shortcomings of the previous two strategies in the context of FP-growth.

show abstract

Using Condensed Representations for Interactive Association Rule Mining

Cited by 21 publications

References 16 publications

Effective elimination of redundant association rules

Effective elimination of redundant association rules

Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining

Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth

Contact Info

Product

Resources

About