Interestingness measures for association rules based on statistical validity

Shaharanee, Izwan Nizal Mohd; Hadzic, Fedja; Dillon, Tharam S.

doi:10.1016/j.knosys.2010.11.005

Cited by 52 publications

(38 citation statements)

References 23 publications

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“…In [3], Shaharanee et al proposed the application of objective analysis to assess the generated rules. The approach consists in combining data mining and statistical measurement techniques (such as redundancy analysis, sampling and multivariate statistical analysis) to discard the insignificant rules.…”

Section: Objective and Subjective Methods For Ar's Post-processingmentioning

confidence: 99%

Post Analysis Based AKD Using Domain Ontology for Disease Prediction

Jeyanthi¹,

Pannirselvam²

2017

IOSR JCE

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Section: Objective and Subjective Methods For Ar's Post-processingmentioning

confidence: 99%

Post Analysis Based AKD Using Domain Ontology for Disease Prediction

Jeyanthi¹,

Pannirselvam²

2017

IOSR JCE

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“…To build classifier, they use a database coverage threshold to select the rules. Classification based on predictive association rules, a statistical-based approach using CARs, was also proposed [27]. Thabtah et al used multi-class, multi-label association classification to mine and predict class of new records [28,29].…”

Section: Mining Class Association Rulesmentioning

confidence: 99%

“…Chen et al proposed a principal association mining (PAM) method to improve the accuracy and the size of classifier [4]. Some efficient methods were also proposed to improve the accuracy such as: using CBA to handle class imbalance [3] and uncertain datasets [10], methods that uses interestingness measures [11,27], a method that uses rule prioritization [5], and a method that uses closed sets [15].…”

Section: Mining Class Association Rulesmentioning

confidence: 99%

A Method for Query Top-K Rules from Class Association Rule Set

Nguyen

Nguyễn²,

et al. 2016

Intelligent Information and Database Systems

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Abstract. Methods for mining/querying Top-k frequent patterns and Top-k association rules have been developed in recent years. However, methods for mining/querying Top-k rules from a set of class association rules have not been developed. In this paper, we propose a method for querying Top-k class association rules based on the support. From the set of mined class association rules that satisfy the minimum support and minimum confidence thresholds, we use an insertion-based method to query Top-k rules. Firstly, we insert k rules from the rule set to the result set. After that, for each rule in the rest, we insert it into the result rule set using the idea of insertion strategy if its support is greater than the support of the last rule in the result rule set. Experimental results show that the proposed method is more efficient than obtaining the result after sorting the whole rule set.

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“…They can reduce the size of the search space and select useful or interesting rules from the set of discovering ones. Many studies have examined the interestingness measures for evaluating association rules and classification rules [1][2][3][4][5][6], but have not been devoted to mine sequential rules in sequence databases except the traditional measures of rule such as the support and confidence [7][8][9][10][11], which was specifically described in Section 2.5. In this chapter, we thus consider and apply several interestingness measures to generate all relevant sequential rules from a sequence database.…”

Section: Introductionmentioning

confidence: 99%