Incremental Mining of Constrained Association Rules

Ayad, Ahmed; El-Makky, Nagwa M.; Taha, Yousry

doi:10.1137/1.9781611972719.24

Cited by 18 publications

(12 citation statements)

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“…A number of algorithms have been developed for searching these rules [2,4,19]. In the next section, we will give a brief introduction to this family of algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective rule mining using genetic algorithms

Ghosh¹,

Nath²

2004

Information Sciences

197

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Association rule mining problems can be considered as a multi-objective problem rather than as a single objective one. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions present in the rule. This objective gives the accuracy of the rules extracted from the database. Comprehensibility is measured by the number of attributes involved in the rule and tries to quantify the understandability of the rule. Interestingness measures how much interesting the rule is.Using these three measures as the objectives of rule mining problem, this article uses a Pareto based genetic algorithm to extract some useful and interesting rules from any market-basket type database. Based on experimentation, the algorithm has been found suitable for large databases.

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“…A number of algorithms have been developed for searching these rules [2,4,19]. In the next section, we will give a brief introduction to this family of algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective rule mining using genetic algorithms

Ghosh¹,

Nath²

2004

Information Sciences

197

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“…Since TIDLists consist of transaction identifiers sorted in an increasing order, the intersection can be performed easily. The procedure is exactly the same as the merge phase of merge sort 41…”

Section: Incremental Association Rule Miningmentioning

confidence: 99%

Incremental association rule mining: a survey

Nath

Bhattacharyya

Ghosh

2013

WIREs Data Min & Knowl

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Association rule mining is a computationally expensive task. Despite the huge processing cost, it has gained tremendous popularity due to the usefulness of association rules. Several efficient algorithms can be found in the literature. This paper provides a comprehensive survey on the state‐of‐the‐art algorithms for association rule mining, specially when the data sets used for rule mining are not static. Addition of new data to a data set may lead to additional rules or to the modification of existing rules. Finding the association rules from the whole data set may lead to significant waste of time if the process has started from the scratch. Several algorithms have been evolved to attend this important issue of the association rule mining problem. This paper analyzes some of them to tackle the incremental association rule mining problem. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification

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“…Many variants of mining association rules are studied to explore more mining capabilities, such as incremental updating (Ayad et al, 2001), mining of generalized and multi-level rules (Lee and Lin, 2001), mining associations among correlated or infrequent items (Agrawal and Srikant, 1995), and temporal association rule discovery (Ale and Rossi, 2000;Chen and Petr, 2000). We mention in passing that the works in Lin and Dunham (1998) and Park and Chen (1997), are essentially based on a partition-based heuristic, i.e.…”

Section: Related Workmentioning

confidence: 99%

PPCI Algorithm for Mining Temporal Association Rules in Large Databases

Pandey

Pardasani

2009

J. Info. Know. Mgmt.

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In this paper an attempt has been made to develop a progressive partitioning and counting inference approach for mining association rules in temporal databases. A temporal database like a sales database is a set of transactions where each transaction T is a set of items in which each item contains an individual exhibition period. The existing models of association rule mining have problems in handling transactions due to a lack of consideration of the exhibition period of each individual item and lack of an equitable support counting basis for each item. As a remedy to this problem we propose an innovative algorithm PPCI that combines progressive partition approach with counting inference method to discover association rules in a temporal database. The basic idea of PPCI is to¯rst segment the database into sub-databases in such a way that items in each sub-database will have either a common starting time or a common ending time. Then for each sub-database, PPCI progressively¯lters 1-itemset with a cumulative¯ltering threshold based on vital partitioning characteristics. Algorithm PPCI is also designed to employ a¯ltering threshold in each partition to prune out those cumulatively infrequent 1-itemsets early and it also uses counting inference approach to minimise as much as possible the number of pattern support counts performed when extracting frequent patterns. Explicitly the execution time of PPCI in order of magnitude is smaller than those required by the schemes which are directly extended from existing methods.

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Incremental Mining of Constrained Association Rules

Cited by 18 publications

References 0 publications

Multi-objective rule mining using genetic algorithms

Multi-objective rule mining using genetic algorithms

Incremental association rule mining: a survey

PPCI Algorithm for Mining Temporal Association Rules in Large Databases

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